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REVERSIBLE DATA HIDING USING CHAOTIC
AND 2D LOGISTIC ENCRYPTION
PROJECT REPORT
PHASE-II
Submitted by
DHASHARATHIR
Register No 14MCO008
in partial fulfillment for the requirement of award of the degree
of
MASTER OF ENGINEERING
in
COMMUNICATION SYSTEMS
Department of Electronics and Communication Engineering
KUMARAGURU COLLEGE OF TECHNOLOGY
(An autonomous institution affiliated to Anna University Chennai)
COIMBATORE - 641 049
ANNA UNIVERSITY CHENNAI 600 025
APRIL - 2016
BONAFIDE CERTIFICATE
Certified that this project report titled ldquoREVERSIBLE DATA HIDING USING
CHAOTIC AND 2D LOGISTIC ENCRYPTIONrdquo is the bonafide work of
DHASHARATHIR [Reg No 14MCO008] who carried out the research under my
supervision Certified further that to the best of my knowledge the work reported herein
does not form part of any other project or dissertation on the basis of which a degree or
award was conferred on an earlier occasion on this or any other candidate
SIGNATURE SIGNATURE DrAAmsaveni Dr AVasuki
PROJECT SUPERVISOR HEAD OF THE DEPARTMENT Department of ECE Department of ECE Kumaraguru College of Technology Kumaraguru College of Technology COIMBATORE - 641049 COIMBATORE -641049
The Candidate with university Register No 14MCO008 was examined by us in
the project viva ndashvoice examination held on
INTERNAL EXAMINER EXTERNAL EXAMINER
ii
ACKNOWLEDGEMENT
First I would like to express my praise and gratitude to the Lord who has
showered his grace and blessings enabling me to complete this project in an excellent
manner
I express my sincere thanks to the management of Kumaraguru College of
Technology and Joint Correspondent Shri Shankar Vanavarayar for his kind support
and for providing necessary facilities to carry out the work
I would like to express my sincere thanks to our beloved Principal DrRSKumar
PhD Kumaraguru College of Technology who encouraged me with his valuable
thoughts
I would like to thank DrAVasuki PhD Head of the Department Electronics
and Communication Engineering for her kind support and for providing necessary
facilities to carry out the project work
I wish to thank with everlasting gratitude to the project coordinator
DrMAlagumeenaakshi PhD Asst Professor(SRG) Department of Electronics and
Communication Engineering throughout the course of this project work
I am greatly privileged to express my heartfelt thanks to my project guide
DrAAmsaveni PhD Associate Professor Department of Electronics and
Communication Engineering for her expert counseling and guidance to make this project
to a great deal of success and I wish to convey my deep sense of gratitude to all teaching
and non-teaching staffs of ECE Department for their help and cooperation
Finally I thank my parents and my family members for giving me the moral
support and abundant blessings in all of my activities and my dear friends who helped me
to endure my difficult times with their unfailing support and warm wishes
iii
ABSTRACT
Data hiding is a technique used in the field of information security Using this
technique secret data can be embedded inside a cover medium by the sender and the
secret data and cover medium can be extracted without any distortion by the receiver The
main benefit of this technique is that the cover medium used for embedding can also be
recovered with high quality Data hiding has a wide range of applications such as medical
image sharing multimedia archive management image transcoding video error
concealment and military application According to the problems of steganography the
main effort is to provide a better imperceptibility of stego-image that can be done by
decreasing distortion of image
The proposed method provides a data hiding technique based on Chaotic and 2D
Logistic encryption The cover image is divided into a number of blocks and 2D logistic
map is created The data to be embedded is encrypted using chaos encryption technique
Finding the best location to hide the secret data is an important task so that it will conceal
the existence of the message The optimal location to hide the secret data is embedding
on the LSB bits This results in a stego image which is not only good in quality but is also
able to sustain certain noise After embedding the secret data into cover medium the
symmetric key is applied The resultant binary pixels are converted into DNA sequence
for additional level of security Attacks have been applied on the image The reverse
process is done to convert the stego image into original image and data can also be
extracted This proposed method ensures the three essential properties which are
commonly used to determine quality of data hiding scheme They are imperceptibility
robustness reversibility and security
The performance metrics like Peak Signal to Noise Ratio (PSNR) Mean Square Error
(MSE) Average Difference (AD) Structural Content (SC) Laplacian Mean Squared
Error (LMSE) Normalized Absolute Error (NAE) and Normalized Correlation
Coefficient (NCC) have been evaluated
iv
TABLE OF CONTENTS
CHAPTER NO TITLE PAGE NO
ABSTRACT iv
LIST OF FIGURES vii
LIST OF TABLES viii
LIST OF ABBRIVEATION viii
1 INTRODUCTION 1
11Cryptography 1
12 Steganography 3
13 Reversible Data Hiding 5
2 LITERATURE SURVEY 6
3 PROPOSED METHODOLOGY 14
31 Chaotic Encryption 15
32 The RSA Algorithm 15
321 Key Generation 16
322 Encryption 16
323 Decryption 16
33 2D Logistic Encryption 17
34 DNA Sequence 18
35 Attacks 19
351 Shearing 20
352 Image Scaling 20
353 Rotation 21
354 Colour Reduced Image 21
355 Blur Image 22
356 Flipped Image 23
357 Cropped Image 23
358 Intensity Transformation Image 24
359 Sharpening 25
3510 Gaussian Noise and Median Filtering 25
3511 Histogram of Contrast Image 26
3512 Speckle Noise and Median Filtering 27
36 Proposed Algorithm 28
4 RESULTS AND DISCUSSIONS 295
5 CONCLUSION AND FUTURE WORK 35
51 Conclusion 35
52 Future Work 35
REFERENCES 36
LIST OF PUBLICATIONS 38
vi
LIST OF TABLES
PAGE NO
41 Performance Metric Calculation 33
42 Performance Metric Calculation between original and 34 recovered Barbara image
LIST OF ABBREVIATIONS
2D 2 Dimensional
AD Average Difference
BER Bit Error Rate
LMSE Laplacian Mean Square Error
LSB Least Significant Bit
MD Maximum Difference
MSE Mean Square Error
NCC Normalized Cross Correlation
PSNR Peak Signal to Noise Ratio
SC Structural Content
viii
TABLE TITLE
NO
2
vii
LIST OF FIGURES
FIGURE
NO
CAPTION
PAGE
NO
11 Symmetric-key cryptography 2
12 Public key Cryptography 2
13 Categories of Image Steganography 4
14 Reversible Data Hiding System 5
31 Work Flow Diagram 14
32 Shearing Image 20
33 Scaling Image 20
34 Rotation image 21
35 Colour Reduced Image 22
36 Blur Image 22
37 Flipped Image 23
38 Cropped Image 24
39 Intensity Transformation Image 24
310 Sharpened Image 25
311 Gaussian Noise and Median Filter Image 26
312 Contrast Image 26
313 Histogram of Contrast Image 27
314 Speckle Noise and Median Filter Image 27
41 Gray Scale Cover image of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
31
42 Input Image and 2D Logistic Encrypted Image 32
43 DNA Sequence 32
44 Recovered Image 33
44 Recovered Text 33
2
1
CHAPTER 1
INTRODUCTION
In an information sharing environment security of information plays an important
role Some information that is sensitive or confidential in nature must be kept private
With the introduction of computers the need for automated tools for protecting files and
other information stored in the computer become evident Transmission of sensitive
information via an open internet channel increases the risk of interception There are
many techniques proposed to deal with this issue They are
1) Cryptography
2) Steganography
3) Reversible Data Hiding
11 CRYPTOGRAPHY
Cryptography is the practice and study of techniques for secure communication in
the presence of third parties (called adversaries) More generally it is about constructing
and analyzing protocols that overcome the influence of adversaries This technique alters
the form of the message at the sender and transmits it At the receiver the original
message is extracted It mainly involves 2 operations
Encryption It is the process of the conversion of information from a readable state to
apparent nonsense with the usage of a key It is done by the sender
Decryption It is the reverse process of encryption That is it is the process of converting
scrambled message into the original one with the help of key The key may be similar to
the one which is used in encryption or it may be a different one It is done at the receiver
side
The cryptography is characterized by 3 independent dimensions
2
1) The type of operations used for transforming Plaintext to Cipher text
All encryption algorithms are based on two general principles They are
substitution and transposition Substitution is the one in which each element in the plain
text is transformed into another element Transposition is the one in which elements in
the plain text are rearranged The fundamental condition is that no information be lost
2) The Number of keys used
Based on this we can classify the techniques into two
a) Symmetric-key Cryptography Symmetric-key cryptography refers to encryption
methods in which both the sender and receiver share the same key (or less commonly in
which their keys are different but related in an easily computable way)
Figure 11 Symmetric-key cryptography
b) Public key Cryptography In public-key cryptosystems the public key may be freely
distributed while its paired private key must remain secret In a public-key encryption
system the public key is used for encryption while the private or secret key is used for
decryption
Figure 12 Public key Cryptography
3
3) The way in which the plaintext is processed
There are 2 types
a) Block Cipher It processes the input one block of elements at a time producing an
output block for each input block
b) Stream Cipher It processes the input elements continuously producing output one
element at a time as it goes along
12 STEGANOGRAPHY
It is the art and science of encoding hidden messages in such a way that no one
apart from the sender and intended recipient suspects the existence of the message It is a
form of security through obscurity Generally the hidden messages will appear to be (or
be part of) something else images articles shopping lists or some other cover texts
Plainly visible encrypted messages no matter how unbreakable will arouse interest and
may in themselves be incriminating in countries where encryption is illegal For example
the hidden message may be in invisible ink between the visible lines of a private letter
The advantage of steganography over cryptography alone is that the intended secret
message does not attract attention to itself as an object of scrutiny So cryptography is the
practice of protecting the contents of a message alone steganography is concerned with
concealing the fact that a secret message is being sent as well as concealing the contents
of the message Steganography includes the concealment of information within computer
files In digital steganography electronic communications may include steganographic
coding inside of a transport layer such as a document file image file program or
protocol Media files are ideal for steganographic transmission because of their large size
There has been a rapid growth of interest in steganography for two main reasons
(i) The publishing and broadcasting industries have become interested in techniques for
hiding encrypted copyright marks and serial numbers in digital films audio
recordings books and multimedia products
(ii) Moves by various governments to restrict the availability of encryption services
have motivated people to study methods by which private messages can be
4
embedded in seemingly innocuous cover messages
Fig 13 Categories of Image Steganography
There are many applications for digital steganography of image including
copyright protection feature tagging and secret communication Copyright notice or
watermark can embedded inside an image to identify it as intellectual property If
someone attempts to use this image without permission we can prove by extracting the
watermark In feature tagging captions annotations time stamps and other descriptive
elements can be embedded inside an image Copying the stegondashimage also copies of the
embedded features and only parties who posses the decoding stego-key will be able to
extract and view the features On the other hand secret communication does not advertise
a covert communication by using steganography Therefore it can avoid scrutiny of the
sender message and recipient This is effective only if the hidden communication is not
detected by the others people In general steganography is two types reversible and
irreversible
5
13 Reversible Data Hiding
Figure 14 Reversible Data Hiding System
Secret Message The secret message or information to hide
Cover File Digital Medium The data or medium which concealed the secret message
Stego File A modified version of cover that contains the secret message
Key Additional secret data that is needed for the embedding and extracting processes
and must be known to both the sender and the recipient
Steganographic Method A steganographic function that takes cover secret message
and key as parameters and produces stego as output
Inverse of Steganographic Method A steganographic function that has stego and key
as parameters and produces secret message as output This is the inverse of method used
in embeding process in the sense that the result of the extracting process is identical to the
input of the embedding process
6
CHAPTER 2
LITERATURE SURVEY
1) Nidhi Sethi Deepika Sharma (2014) ldquoA Novel Method Of Image
Encryption using Logistic Mappingrdquo International Journal of Computer
Science Engineering (IJCSE)
This paper presents a new method to develop secure image-encryption techniques
using a logistics based encryption algorithm In this technique a Haar wavelet transform
was used to decompose the image and decorrelate its pixels into averaging and
differencing components The logistic based encryption algorithm produces a cipher of
the test image that has good diffusion and confusion properties The remaining
components (the differencing components) are compressed using a wavelet transform
Many test images are used to demonstrate the validity of the proposed algorithm The
results of several experiments show that the proposed algorithm for image cryptosystems
provides an efficient and secure approach to real-time image encryption and transmission
To send the keys in secure form steganography will be used Steganographic techniques
allow one party to communicate information to another party without a third party even
knowing that the communication is occurring
Advantages
(i) Efficient approach
(ii) Secure key transmission
(iii) Better image quality
7
2) Jing Wang Guoping Jiang (2013) ldquoA Self-Adaptive Parallel Encryption
Algorithm Based on Discrete 2D-Logistic Maprdquo International Journal of
Modern Nonlinear Theory and Application
A self-adaptive parallel encryption algorithm based on discrete 2D-Logistic
map is developed according to the position scrambling and diffusion of multi-direction in
variable space of spatial chaos The binary sequences b1b2b3bn are obtained according
to the user key in which the binary sequence 0 and 1 denote distribution mode of
processors and the number of binary sequence n denotes cycle number Then the
pseudorandom 2D matrix is generated by 2D-Logistic map and adaptive segmentation is
applied in image matrix and pseudorandom matrix according to the value and the number
of binary sequence The parallel operation is used among blocks to improve efficiency
and meet real-time demand in transmission processes However the pixel permutation is
applied in partitioned matrix through ergodic matrix generated by pseudo-random matrix-
block to decrease the correlation of adjacent pixels Then the pixel substitution is used for
fully diffusing through cipher block chaining mode until n cycles The proposed
algorithm can meet the three requirements of parallel operation in image encryption and
the real-time requirement in transmission processes The security is proved by theoretical
analysis and simulation results
Advantages
1Security is provided
2Effeciency is improved
8
3) LJani Anbarasi GSAnandha Mala Modigari Narendra (2014) ldquoDNA
based Multi-Secret Image Sharingrdquo International Conference on
Information and Communication Technologies (ICICT)
Multiple secret sharing algorithm using the YCH scheme combined with
DNA encoding is proposed focusing at better security Firstly DNA encoding for
multiple images is carried out then the addition of these encoded components by DNA is
performed Secondly the (t n) scheme used the Lagrange interpolation polynomial to
share these DNA scrambled matrices is performed bdquot‟ denotes threshold value and bdquon‟
denotes the number of participants The resulting scrambled images are encrypted into n
shares using Shamir‟s polynomial Thirdly these shares are embedded using a modular
operation Finally bdquot‟ or more shares are pooled which reconstructs the scrambled
matrices and by decoding the DNA scrambled matrices multiple secrets are
reconstructed without loss The simulation results and the security analysis prove that this
algorithm is perfect and produces results with better PSNR value The correlation co-
efficient shows that this also has the ability of resisting various attacks
Advantages
1Security is better
2Resistance against Attack
9
4)Della Babya Jitha Thomasa Gisny Augustinea Elsa Georgea Neenu
Rosia Michaela (2014) ldquoA Novel DWT based Image Securing Method using
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Steganography is a data hiding technique that is widely used in various
information securing applications Steganography transmits data by hiding the existence
of the message so that a viewer cannot identify the transmission of message and hence
not able to decrypt it This work proposes a data securing technique that is used for
hiding multiple color images into a single color image using the Discrete Wavelet
Transform The cover image is split up into R G and B planes Secret images are
embedded into these planes An N-level decomposition of the cover image and the secret
images are done and some frequency components of the same are combined Secret
images are then extracted from the stego image Here the stego image obtained has a less
perceptible changes compared to the original image with high overall security
Advantages
1Less perceptible changes
2Overall security is high
10
5)Prasenjit Dasa Subhrajyoti Deba Nirmalya Kara Baby
Bhattacharyaa(2014)ldquoAn Improved DNA based Dual Cover
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Dual cover steganography is an evolving technique in the field of covert
data transmission This paper focuses on the concept of using a theoretical single stranded
DNA (ssDNA) as a primary cover which is extracted from an inconspicuous cover
image They have analyzed the security loopholes and performance issues of the existing
algorithm and proposed an improved algorithm on the same basisthey use 2D Logistic
map for encrypting the cover imageThen overall encryption is RC43 types of encryption
is generally used Performance of both the algorithms are tested against several visual
and statistical attacks and parameterized in terms of both security and capacity The
comparison shows that the proposed improvements provide better overall security
Advantages
1 Robustness against various attack
2 Performance measure are calculated
3 Data hiding improves security
11
6) Bhavana Agrawal Himani Agrawal ( May-2013) ldquoImplementation of AES
and RSA Using Chaos systemrdquo International Journal of Scientific amp
Engineering Research Vol 4 No 5
This paper presents two cryptographic algorithm AES and RSA Using Chaos
Chaos has attracted much attention in the field of cryptography It describes a system
which is sensitive to initial condition It generates apparently random behavior but at the
same time is completely deterministic Chaos function is used to increase the complexity
and Security of the SystemAES and RSA are the two cryptographic algorithms In AES
we apply the Chaos on S-box where as in RSA we mix the plaintext with Chaos sequence
First then apply for encryption and decryption After Implementing AES and RSA they
compare both the technique on the basis of speed
Advantages
1Chaos function is used to improve complexity
2The speed has been improved with combined technique of AES and RSA along with
chaos technique
12
7) Pahrul Irfan Yudi Prayudi Imam Riadi ( August 2015) ldquo Image
Encryption using Combination of Chaotic System and Rivers Shamir
Adleman (RSA)rdquo International Journal of Computer Applications Vol 123
No6
Security and confidentiality of data or information at the present time has
become an important concern Advanced methods for secure transmission storage and
retrieval of digital images are increasingly needed for a number of military medical
homeland security and other applications Various kinds of techniques for increase
security data or information already is developed one common way is by cryptographic
techniques Cryptography is science to maintain the security of the message by changing
data or information into a different form so the message cannot be recognized To
compensate for increasing computing speeds increases it takes more than one encryption
algorithm to improve security of digital images One way is by using algorithms to
double cryptography do encryption and decryption Cryptographic algorithm often used
today and the proven strength specially the digital image is Algorithm with Chaos
system To improve security at the image then we use Additional algorithms namely
Rivers algorithm Shamir Adleman (RSA) which known as the standard of cryptography
algorithms This research aims to optimize security bitmap image format by combining
the two algorithms namely Chaos-based algorithms and RSA algorithm into one
application Experiments conducted show that the proposed algorithm possesses robust
security features such as fairly uniform distribution high sensitivity to both keys and
plain images almost ideal entropy and the ability to highly de-correlate adjacent pixels
in the cipher images Furthermore it has a large key space and transform image to pure
text file which greatly increases its security for image encryption
Advantages
1 It aims to optimize security bitmap image format by combining the two algorithms
namely Chaos-based algorithms and RSA algorithm into one application
13
8) Weiming Zhang KedeMa NenghaiYu(2013) ldquo Reversibility improved
data hiding in encrypted imagesrdquo School of Information Science and
Technology
A novel reversible data hiding technique in encrypted images is presented in this
paper Instead of embedding data in encrypted images directly some pixels are estimated
before encryption so that additional data can be embedded in the estimating errors A
bench mark encryption algorithm (eg AES) is applied to the rest pixels of the image and
a special encryption scheme is designed to encrypt the estimating errors Without the
encryption key one cannot get access to the original image However provided with the
data hiding key only he can embed in or extract from the encrypted image additional data
without knowledge about the original image Moreover the data extraction and image
recovery are free of errors for all images Experiments demonstrate the feasibility and
efficiency of the proposed method especially in aspect of embedding rate versus Peak
Signal-to-Noise Ratio (PSNR)
The paper proposes a novel method to significantly improve the performance by
reversing the order of encryption and vacating room In the light of this idea we empty
out room prior to image encryption by shifting the histogram of estimating errors of some
pixels and the emptied out room will be used for data hiding The proposed method is
composed of four primary steps vacating room and encrypting image data hiding in the
encrypted image data extraction and image recovery Two different schemes extraction
before decryption and decryption before extraction are raised to cope with different
applications
Advantages
(i) Achieves excellent performance in three aspects complete reversibility PSNR
under given embedding rate separability between data higher extraction and
image decryption
14
CHAPTER 3
PROPOSED METHODOLOGY
The proposed data hiding scheme aims at the security of the hidden data
Embedding is performed in spatial domain The data to be embedded is converted into
binary form from ASCII code using chaos encryption and is embedded into the cover
image obtained after 2D logistic map This embedded image is secured using symmetric
key (K1)They are converted into DNA sequence to provide additional level of security
The hidden data can be extracted from the cover image accurately with the help of
decryption techniques and secret key (K1) The cover image can also be extracted
without any distortion The fig 31 shows the workflow
Fig 31 Work Flow Diagram
SECRET DATA
COVER IMAGE
CHAOTIC
ENCRYPTION
ENCRY 2D LOGISTIC
ENCRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
CHAOTIC
DECRYPTION
ENCRY
SECRET DATA
COVER IMAGE 2D LOGISTIC
DECRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
15
31 Chaotic Encryption
Chaotic cryptography is the application of the mathematical chaos theory to the
practice of the cryptography the study or techniques used to privately and securely
transmit information with the presence of an third-party or adversary The use of chaos
or randomness in cryptography has long been sought after by entities wanting a new way
to encrypt messages However because of the lack of thorough provable security
properties and low acceptable performance chaotic cryptography has encountered
setbacksIn order to use chaos theory acceptably in cryptography they must first be
mapped to each other Properties in chaotic systems and cryptographic primitives share
unique characteristics that allow for the chaotic systems to be applied to cryptography If
chaotic parameters as well as cryptographic keys can be mapped symmetrically or
mapped to produce acceptable and functional outputs it will make it next to impossible
for an adversary to find the outputs without any knowledge the initial values Since
chaotic maps in a real life scenario require a set of numbers that are limited they may in
fact have no real purpose in a cryptosystem if the chaotic behavior can be predicted To
counter this possibility there exists simple to advanced ciphers Chaos theory used in
cryptosystems for commercial implementation has proven to be unsuccessful mainly
because a chaos theories‟ requirement to use intervals of real numbers Given enough
resources and time an adversary could be able to predict functional outcomes Since
chaotic cryptosystems have no root in number theory this would make it difficult or
impossible to implement therefore impractical
32 The RSA Algorithm
The RSA cryptosystem named after its inventors R Rivest A Shamir and L
Adleman is the most widely used public key Cryptosystem It may be used to provide
both secrecy and digital signatures and its security is based on the intractability of the
integer factorizationThe RSA algorithm involves three steps key generation encryption
and decryption
16
321 Key Generation
RSA involves a public key and a private key The public key can be known to
everyone and is used for encrypting messages Messages encrypted with the public key
can only be decrypted in a reasonable amount of time using the private key The keys for
the RSA algorithm are generated the following way To generate the two keys choose
two random large prime numbers p and q For maximum security choose p and q of
equal length Then randomly choose the encryption key e such that e and ( p minus1) (q minus1)
are relatively prime Finally use the extended Euclidean algorithm to compute the
decryption key d such that
d= e-1
mod ( (p-1) (q-1))
Note that d and n are also relatively prime The numbers e and K are the public
key the number d is the private key The two primes p and q are no longer needed They
should be discarded but never revealed
322 Encryption
Firstly receiver transmits her public key (n e) to sender and keeps the private key
secret If sender wishes to send message M to receiver Sender change the message M in
to integer m such that 0 le mltn Then sender computes the cipher text c corresponding to
Cequiv me
(mod n)
323 Decryption
Receiver can recover M from c by using private key exponent d via computing
M equiv cd
(mod n)
17
Algorithm
1Select any two prime numbers say (pq)
2Compute n=pq and also compute empty(119899)=(p-1)(q-1)
3Choose e such that 1ltelt empty(119899)
4Choose d such that (de)mod empty(119899)=1
5Public key is (en) and Private key is (dn)
6 If egt=2 then check i==1 if so return 1 else return 0
7In a iteration check for e(i)==1 if so take mod function of message with n
8Message is converted to cipher text in ASCII form with the key generated
9The cipher data in ASCII form is converted to binary form
33 2D Logistic Encryption
The chaotic system is a deterministic nonlinear system It possesses a varied
characteristics such as high sensitivity to initial conditions and system parameters
random-like behaviors and so forth Chaotic sequences produced by chaotic maps are
pseudo-random sequences their structures are very complex and difficult to be analyzed
and predicted In other words chaotic systems can improve the security of encryption
systems Thus it is advisable to encrypt digital image with chaotic systems There are
two chaotic maps one is logistic map and the other is 2D logistic map In the proposed
work 2D logistic map is used
Logistic map is an example for chaotic map and it is described as follows
x(n+1)=μ x(n)(1minusx(n))
μ is a positive constant sometimes known as the biotic potential gives the so-called
logistic map x(n) is series of values used to plot Logistic mapwhere μ isin [04] x(n) isin
(01) and n = 01 2hellip The research result shows that the system is in chaotic state
under the condition that 356994 lt μ le 4
18
2D logistic map is described in as follows
119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2
119910119894+1 = 1205832 lowast 119910119894(1 minus 119910119894) + 1205732((119909119894)2 + 119909119894 lowast 119910119894)
Where z(xy) is the logistic map 1205831 and 1205832 are positive constant sometimes known as the
biotic potential gives the so-called logistic map x be the position of images in x axis
and y be the position of images in y axis 1205731 and 1205732 are the correlation constantsWhen
275 lt μ1 le 34 275 lt μ2 le 345 015 lt 1205731le 021 and 013 lt 1205732 le 015 the system is in
chaotic state and can generate two chaotic sequences in the region (01] Due to the
system parameter γ1 and γ2 which have smaller value range we set γ1 = 017 and γ2 =
014 other parameters can be seen as secret keys
Algorithm
1A random key is generated in binary form ( 1times256) and it is stored in a array
2The random key generated is translated to map format using block processing (4times4)
3 The row and column wise transformation is carried out
4The key is now used to encrypt the cover image
52D logistic image undergoes substitution and permutation (column and row wise
shuffling is done)
34 DNA Sequence
A single DNA sequence is made up of four nucleic acid
bases A (adenine) C (cytosine) G (guanine) and T (thymine) where A and T are
complements and C and G are complements Let binary number 0 and 1 be
complements so 00 and 11 are complements and 01 and 10 are complements Thus we
can use these four bases A T G and C to encode 01 10 00 and 11 respectively The
encoding method still satisfies the Watson-Crick complement rule Usually each pixel
value of the 8 bit grey image can be expressed to 8 bits binary stream The binary stream
can be encoded to a DNA sequence whose length is 4 For example if the first pixel
19
value of the original image is 75 convert it into a binary stream [01001011] By using the
above DNA encoding rule to encode the stream we can get a DNA sequence [AGTC]
whereas we use A T G and C to express 01 10 00 and 11 respectively We can get a
binary sequence [01001011]
35 Attacks
The steganographic algorithm is used to embed secret messages into cover
image To obtain stego image while exchanging these stego-image through the public
communication channel various attacks have been made The are generally classified
into two types intentional or unintentional attacks Examples of unintentional attacks are
transmission errors lossy compression and changing the visual properties of the stego
document Intentional attacks on the other hand are deliberate attempts to distinguish
stego-objects from unmodified objects and thus detect the presence of covert
communication Attack methods generally exploit the fact that embedding information
usually changes the statistical properties of the objects compared to typical unmodified
objects In this proposed algorithm various attacks have been applied on the encrypted
image They are as follows
1)Shearing
2) Image Scaling
3) Image Rotating
4) Image color reduction
5) Image blurred
6) Image flip
7) cropping and intensity transformation
8) Image sharpening
9) Gaussian Noise and filtering
10) Image Contrast
11) Speckle Noise and Filtering
20
351 Shearing
The image is resized with the scale factor Resized image is rotated with
angle(theta)Finally spatial transformation from control point pairs is implemented
For example
Scale factor 09
Theta10
Fig 32 Shearing Image
352 Image Scaling
It resizes the image with a scale factor and rotation is performed It rotates the
image by angle (degrees) in a counterclockwise direction around its center point To
rotate the image clockwise specify a negative value for angle It makes the output image
large enough to contain the entire rotated image It uses nearest neighbour interpolation
setting the values of pixels in Output image that are outside the rotated image to 0 (zero)
For example
Scale Factor07
Theta30
scaling Image
21
Fig 33 Scaling Image
353 Rotation
It rotates the image by angle degrees in a counterclockwise direction around its
center point To rotate the image clockwise specify a negative value for angle It makes
the output image large enough to contain the entire rotated image It uses nearest
neighbour interpolation setting the values of pixels in Output image that are outside the
rotated image to 0 (zero)
For Example
Theta180
Fig 34 Rotation Image
354 Colour Reduced Image
It creates an indexed image approximation of the RGB image in the array RGB by
dithering the colors in the colormap map The colormap cannot have more than 65536
resized and rotated image
Rotated image
22
colors
For Example
Indexed image with 32 Colors
Fig 35 Colour Reduced Image
355 Blur Image
The image is blurred by using N-D filtering of multidimensional images It filters
the multidimensional array of original image with the multidimensional filter The array
of original image can be logical or a nonsparse numeric array of any class and dimension
The result image has the same size and class as of original image
Fig 36 Blur Image
Color reduced image
50 100 150 200 250 300 350 400 450 500
50
100
150
200
250
300
350
400
450
500
Blurred image
23
356 Flipped Image
It flips the image upside down Flipping is used to invert the image
Fig 37 Flipped Image
357 Cropped Image
It creates an interactive crop image tool associated with the image displayed in the
current figure called the target image The crop image tool is a movable resizable
rectangle that you can position interactively using the mouse When the crop image tool
is active the pointer changes to cross hairs when it is moved over the target image
Using the mouse image to be cropped can be specified by clicking and dragging the
mouse The crop rectangle using the mouse can be moved or resized When sizing and
positioning of the crop rectangle is finished create the cropped image by double-clicking
the left mouse button or by choosing crop image from the context menu Image cropping
returns the cropped image
Flipped image
24
Fig 38 Cropped Image
358 Intensity Transformation Adjust
It maps the intensity values in grayscale image to new values in resultant image
such that 1 of data is saturated at low and high intensity of original image This
increases the contrast of the output image
Fig 39 Intensity Transformation Image
Cropped Image
Intensity Transformation
25
359 Sharpening
Input array values outside the bounds of the array are assumed to equal the nearest
array border value The image is sharpened by using N-D filtering of multidimensional
images It filters the multidimensional array of original image with the multidimensional
filter The array of original image can be logical or a nonsparse numeric array of any
class and dimension The result image has the same size and class as of original image
Fig 310 Sharpened Image
3510 Gaussian Noise and Median Filtering
It adds Gaussian noise to the images Gaussian white noise have constant mean
and variance The noise added image is filtered using Median Filtering Median filtering
is a nonlinear operation often used in image processing to reduce salt and pepper noise
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Sharpened Image
26
Fig 311 Gaussian Noise and Median Filter Image
3511 Histogram of contrast image
It enhances the contrast of images by transforming the values in an intensity
image or the values in the colormap of an indexed image so that the histogram of the
output image approximately matches a specified histogram
Fig 312 Contrast Image
Gaussian Noise
Median Filtering
Contrast Image
27
Fig 313 Histogram of Contrast Image
3512 Speckle noise and Median Filtering
It adds multiplicative noise to the image I using the equation J = I+nI where n is
uniformly distributed random noise with mean 0 and variance v The default for v is 004
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Fig 314 Speckle Noise and Median Filter Image
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Histogram of Contrast Image
0 50 100 150 200 250
Speckle Noise
Median Filtering
28
36 Proposed Algorithm
Step1 Enter two keys a private key and a public key through which the RSA algorithm is
performed
Step2These key are used for encrypting the secret data using chaotic algorithm with
threshold of 2
Step3The secret data is converted into binary format from ASCII code
Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied
Step5The encryption is carried out with the key generated randomly in binary(1times256)
Step6 The 2D logistic substitution and permutation are carried out
Step7The resulting binary sequence is added with the encrypted text in LSB
Step8The image is converted to DNA sequence and transmitted
Step9 Various Attacks have been applied on the resultant image
Step10The inverse process is carried out to retrieve the original image and data
Step11The Performance Metrics have been calculated
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
BONAFIDE CERTIFICATE
Certified that this project report titled ldquoREVERSIBLE DATA HIDING USING
CHAOTIC AND 2D LOGISTIC ENCRYPTIONrdquo is the bonafide work of
DHASHARATHIR [Reg No 14MCO008] who carried out the research under my
supervision Certified further that to the best of my knowledge the work reported herein
does not form part of any other project or dissertation on the basis of which a degree or
award was conferred on an earlier occasion on this or any other candidate
SIGNATURE SIGNATURE DrAAmsaveni Dr AVasuki
PROJECT SUPERVISOR HEAD OF THE DEPARTMENT Department of ECE Department of ECE Kumaraguru College of Technology Kumaraguru College of Technology COIMBATORE - 641049 COIMBATORE -641049
The Candidate with university Register No 14MCO008 was examined by us in
the project viva ndashvoice examination held on
INTERNAL EXAMINER EXTERNAL EXAMINER
ii
ACKNOWLEDGEMENT
First I would like to express my praise and gratitude to the Lord who has
showered his grace and blessings enabling me to complete this project in an excellent
manner
I express my sincere thanks to the management of Kumaraguru College of
Technology and Joint Correspondent Shri Shankar Vanavarayar for his kind support
and for providing necessary facilities to carry out the work
I would like to express my sincere thanks to our beloved Principal DrRSKumar
PhD Kumaraguru College of Technology who encouraged me with his valuable
thoughts
I would like to thank DrAVasuki PhD Head of the Department Electronics
and Communication Engineering for her kind support and for providing necessary
facilities to carry out the project work
I wish to thank with everlasting gratitude to the project coordinator
DrMAlagumeenaakshi PhD Asst Professor(SRG) Department of Electronics and
Communication Engineering throughout the course of this project work
I am greatly privileged to express my heartfelt thanks to my project guide
DrAAmsaveni PhD Associate Professor Department of Electronics and
Communication Engineering for her expert counseling and guidance to make this project
to a great deal of success and I wish to convey my deep sense of gratitude to all teaching
and non-teaching staffs of ECE Department for their help and cooperation
Finally I thank my parents and my family members for giving me the moral
support and abundant blessings in all of my activities and my dear friends who helped me
to endure my difficult times with their unfailing support and warm wishes
iii
ABSTRACT
Data hiding is a technique used in the field of information security Using this
technique secret data can be embedded inside a cover medium by the sender and the
secret data and cover medium can be extracted without any distortion by the receiver The
main benefit of this technique is that the cover medium used for embedding can also be
recovered with high quality Data hiding has a wide range of applications such as medical
image sharing multimedia archive management image transcoding video error
concealment and military application According to the problems of steganography the
main effort is to provide a better imperceptibility of stego-image that can be done by
decreasing distortion of image
The proposed method provides a data hiding technique based on Chaotic and 2D
Logistic encryption The cover image is divided into a number of blocks and 2D logistic
map is created The data to be embedded is encrypted using chaos encryption technique
Finding the best location to hide the secret data is an important task so that it will conceal
the existence of the message The optimal location to hide the secret data is embedding
on the LSB bits This results in a stego image which is not only good in quality but is also
able to sustain certain noise After embedding the secret data into cover medium the
symmetric key is applied The resultant binary pixels are converted into DNA sequence
for additional level of security Attacks have been applied on the image The reverse
process is done to convert the stego image into original image and data can also be
extracted This proposed method ensures the three essential properties which are
commonly used to determine quality of data hiding scheme They are imperceptibility
robustness reversibility and security
The performance metrics like Peak Signal to Noise Ratio (PSNR) Mean Square Error
(MSE) Average Difference (AD) Structural Content (SC) Laplacian Mean Squared
Error (LMSE) Normalized Absolute Error (NAE) and Normalized Correlation
Coefficient (NCC) have been evaluated
iv
TABLE OF CONTENTS
CHAPTER NO TITLE PAGE NO
ABSTRACT iv
LIST OF FIGURES vii
LIST OF TABLES viii
LIST OF ABBRIVEATION viii
1 INTRODUCTION 1
11Cryptography 1
12 Steganography 3
13 Reversible Data Hiding 5
2 LITERATURE SURVEY 6
3 PROPOSED METHODOLOGY 14
31 Chaotic Encryption 15
32 The RSA Algorithm 15
321 Key Generation 16
322 Encryption 16
323 Decryption 16
33 2D Logistic Encryption 17
34 DNA Sequence 18
35 Attacks 19
351 Shearing 20
352 Image Scaling 20
353 Rotation 21
354 Colour Reduced Image 21
355 Blur Image 22
356 Flipped Image 23
357 Cropped Image 23
358 Intensity Transformation Image 24
359 Sharpening 25
3510 Gaussian Noise and Median Filtering 25
3511 Histogram of Contrast Image 26
3512 Speckle Noise and Median Filtering 27
36 Proposed Algorithm 28
4 RESULTS AND DISCUSSIONS 295
5 CONCLUSION AND FUTURE WORK 35
51 Conclusion 35
52 Future Work 35
REFERENCES 36
LIST OF PUBLICATIONS 38
vi
LIST OF TABLES
PAGE NO
41 Performance Metric Calculation 33
42 Performance Metric Calculation between original and 34 recovered Barbara image
LIST OF ABBREVIATIONS
2D 2 Dimensional
AD Average Difference
BER Bit Error Rate
LMSE Laplacian Mean Square Error
LSB Least Significant Bit
MD Maximum Difference
MSE Mean Square Error
NCC Normalized Cross Correlation
PSNR Peak Signal to Noise Ratio
SC Structural Content
viii
TABLE TITLE
NO
2
vii
LIST OF FIGURES
FIGURE
NO
CAPTION
PAGE
NO
11 Symmetric-key cryptography 2
12 Public key Cryptography 2
13 Categories of Image Steganography 4
14 Reversible Data Hiding System 5
31 Work Flow Diagram 14
32 Shearing Image 20
33 Scaling Image 20
34 Rotation image 21
35 Colour Reduced Image 22
36 Blur Image 22
37 Flipped Image 23
38 Cropped Image 24
39 Intensity Transformation Image 24
310 Sharpened Image 25
311 Gaussian Noise and Median Filter Image 26
312 Contrast Image 26
313 Histogram of Contrast Image 27
314 Speckle Noise and Median Filter Image 27
41 Gray Scale Cover image of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
31
42 Input Image and 2D Logistic Encrypted Image 32
43 DNA Sequence 32
44 Recovered Image 33
44 Recovered Text 33
2
1
CHAPTER 1
INTRODUCTION
In an information sharing environment security of information plays an important
role Some information that is sensitive or confidential in nature must be kept private
With the introduction of computers the need for automated tools for protecting files and
other information stored in the computer become evident Transmission of sensitive
information via an open internet channel increases the risk of interception There are
many techniques proposed to deal with this issue They are
1) Cryptography
2) Steganography
3) Reversible Data Hiding
11 CRYPTOGRAPHY
Cryptography is the practice and study of techniques for secure communication in
the presence of third parties (called adversaries) More generally it is about constructing
and analyzing protocols that overcome the influence of adversaries This technique alters
the form of the message at the sender and transmits it At the receiver the original
message is extracted It mainly involves 2 operations
Encryption It is the process of the conversion of information from a readable state to
apparent nonsense with the usage of a key It is done by the sender
Decryption It is the reverse process of encryption That is it is the process of converting
scrambled message into the original one with the help of key The key may be similar to
the one which is used in encryption or it may be a different one It is done at the receiver
side
The cryptography is characterized by 3 independent dimensions
2
1) The type of operations used for transforming Plaintext to Cipher text
All encryption algorithms are based on two general principles They are
substitution and transposition Substitution is the one in which each element in the plain
text is transformed into another element Transposition is the one in which elements in
the plain text are rearranged The fundamental condition is that no information be lost
2) The Number of keys used
Based on this we can classify the techniques into two
a) Symmetric-key Cryptography Symmetric-key cryptography refers to encryption
methods in which both the sender and receiver share the same key (or less commonly in
which their keys are different but related in an easily computable way)
Figure 11 Symmetric-key cryptography
b) Public key Cryptography In public-key cryptosystems the public key may be freely
distributed while its paired private key must remain secret In a public-key encryption
system the public key is used for encryption while the private or secret key is used for
decryption
Figure 12 Public key Cryptography
3
3) The way in which the plaintext is processed
There are 2 types
a) Block Cipher It processes the input one block of elements at a time producing an
output block for each input block
b) Stream Cipher It processes the input elements continuously producing output one
element at a time as it goes along
12 STEGANOGRAPHY
It is the art and science of encoding hidden messages in such a way that no one
apart from the sender and intended recipient suspects the existence of the message It is a
form of security through obscurity Generally the hidden messages will appear to be (or
be part of) something else images articles shopping lists or some other cover texts
Plainly visible encrypted messages no matter how unbreakable will arouse interest and
may in themselves be incriminating in countries where encryption is illegal For example
the hidden message may be in invisible ink between the visible lines of a private letter
The advantage of steganography over cryptography alone is that the intended secret
message does not attract attention to itself as an object of scrutiny So cryptography is the
practice of protecting the contents of a message alone steganography is concerned with
concealing the fact that a secret message is being sent as well as concealing the contents
of the message Steganography includes the concealment of information within computer
files In digital steganography electronic communications may include steganographic
coding inside of a transport layer such as a document file image file program or
protocol Media files are ideal for steganographic transmission because of their large size
There has been a rapid growth of interest in steganography for two main reasons
(i) The publishing and broadcasting industries have become interested in techniques for
hiding encrypted copyright marks and serial numbers in digital films audio
recordings books and multimedia products
(ii) Moves by various governments to restrict the availability of encryption services
have motivated people to study methods by which private messages can be
4
embedded in seemingly innocuous cover messages
Fig 13 Categories of Image Steganography
There are many applications for digital steganography of image including
copyright protection feature tagging and secret communication Copyright notice or
watermark can embedded inside an image to identify it as intellectual property If
someone attempts to use this image without permission we can prove by extracting the
watermark In feature tagging captions annotations time stamps and other descriptive
elements can be embedded inside an image Copying the stegondashimage also copies of the
embedded features and only parties who posses the decoding stego-key will be able to
extract and view the features On the other hand secret communication does not advertise
a covert communication by using steganography Therefore it can avoid scrutiny of the
sender message and recipient This is effective only if the hidden communication is not
detected by the others people In general steganography is two types reversible and
irreversible
5
13 Reversible Data Hiding
Figure 14 Reversible Data Hiding System
Secret Message The secret message or information to hide
Cover File Digital Medium The data or medium which concealed the secret message
Stego File A modified version of cover that contains the secret message
Key Additional secret data that is needed for the embedding and extracting processes
and must be known to both the sender and the recipient
Steganographic Method A steganographic function that takes cover secret message
and key as parameters and produces stego as output
Inverse of Steganographic Method A steganographic function that has stego and key
as parameters and produces secret message as output This is the inverse of method used
in embeding process in the sense that the result of the extracting process is identical to the
input of the embedding process
6
CHAPTER 2
LITERATURE SURVEY
1) Nidhi Sethi Deepika Sharma (2014) ldquoA Novel Method Of Image
Encryption using Logistic Mappingrdquo International Journal of Computer
Science Engineering (IJCSE)
This paper presents a new method to develop secure image-encryption techniques
using a logistics based encryption algorithm In this technique a Haar wavelet transform
was used to decompose the image and decorrelate its pixels into averaging and
differencing components The logistic based encryption algorithm produces a cipher of
the test image that has good diffusion and confusion properties The remaining
components (the differencing components) are compressed using a wavelet transform
Many test images are used to demonstrate the validity of the proposed algorithm The
results of several experiments show that the proposed algorithm for image cryptosystems
provides an efficient and secure approach to real-time image encryption and transmission
To send the keys in secure form steganography will be used Steganographic techniques
allow one party to communicate information to another party without a third party even
knowing that the communication is occurring
Advantages
(i) Efficient approach
(ii) Secure key transmission
(iii) Better image quality
7
2) Jing Wang Guoping Jiang (2013) ldquoA Self-Adaptive Parallel Encryption
Algorithm Based on Discrete 2D-Logistic Maprdquo International Journal of
Modern Nonlinear Theory and Application
A self-adaptive parallel encryption algorithm based on discrete 2D-Logistic
map is developed according to the position scrambling and diffusion of multi-direction in
variable space of spatial chaos The binary sequences b1b2b3bn are obtained according
to the user key in which the binary sequence 0 and 1 denote distribution mode of
processors and the number of binary sequence n denotes cycle number Then the
pseudorandom 2D matrix is generated by 2D-Logistic map and adaptive segmentation is
applied in image matrix and pseudorandom matrix according to the value and the number
of binary sequence The parallel operation is used among blocks to improve efficiency
and meet real-time demand in transmission processes However the pixel permutation is
applied in partitioned matrix through ergodic matrix generated by pseudo-random matrix-
block to decrease the correlation of adjacent pixels Then the pixel substitution is used for
fully diffusing through cipher block chaining mode until n cycles The proposed
algorithm can meet the three requirements of parallel operation in image encryption and
the real-time requirement in transmission processes The security is proved by theoretical
analysis and simulation results
Advantages
1Security is provided
2Effeciency is improved
8
3) LJani Anbarasi GSAnandha Mala Modigari Narendra (2014) ldquoDNA
based Multi-Secret Image Sharingrdquo International Conference on
Information and Communication Technologies (ICICT)
Multiple secret sharing algorithm using the YCH scheme combined with
DNA encoding is proposed focusing at better security Firstly DNA encoding for
multiple images is carried out then the addition of these encoded components by DNA is
performed Secondly the (t n) scheme used the Lagrange interpolation polynomial to
share these DNA scrambled matrices is performed bdquot‟ denotes threshold value and bdquon‟
denotes the number of participants The resulting scrambled images are encrypted into n
shares using Shamir‟s polynomial Thirdly these shares are embedded using a modular
operation Finally bdquot‟ or more shares are pooled which reconstructs the scrambled
matrices and by decoding the DNA scrambled matrices multiple secrets are
reconstructed without loss The simulation results and the security analysis prove that this
algorithm is perfect and produces results with better PSNR value The correlation co-
efficient shows that this also has the ability of resisting various attacks
Advantages
1Security is better
2Resistance against Attack
9
4)Della Babya Jitha Thomasa Gisny Augustinea Elsa Georgea Neenu
Rosia Michaela (2014) ldquoA Novel DWT based Image Securing Method using
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Steganography is a data hiding technique that is widely used in various
information securing applications Steganography transmits data by hiding the existence
of the message so that a viewer cannot identify the transmission of message and hence
not able to decrypt it This work proposes a data securing technique that is used for
hiding multiple color images into a single color image using the Discrete Wavelet
Transform The cover image is split up into R G and B planes Secret images are
embedded into these planes An N-level decomposition of the cover image and the secret
images are done and some frequency components of the same are combined Secret
images are then extracted from the stego image Here the stego image obtained has a less
perceptible changes compared to the original image with high overall security
Advantages
1Less perceptible changes
2Overall security is high
10
5)Prasenjit Dasa Subhrajyoti Deba Nirmalya Kara Baby
Bhattacharyaa(2014)ldquoAn Improved DNA based Dual Cover
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Dual cover steganography is an evolving technique in the field of covert
data transmission This paper focuses on the concept of using a theoretical single stranded
DNA (ssDNA) as a primary cover which is extracted from an inconspicuous cover
image They have analyzed the security loopholes and performance issues of the existing
algorithm and proposed an improved algorithm on the same basisthey use 2D Logistic
map for encrypting the cover imageThen overall encryption is RC43 types of encryption
is generally used Performance of both the algorithms are tested against several visual
and statistical attacks and parameterized in terms of both security and capacity The
comparison shows that the proposed improvements provide better overall security
Advantages
1 Robustness against various attack
2 Performance measure are calculated
3 Data hiding improves security
11
6) Bhavana Agrawal Himani Agrawal ( May-2013) ldquoImplementation of AES
and RSA Using Chaos systemrdquo International Journal of Scientific amp
Engineering Research Vol 4 No 5
This paper presents two cryptographic algorithm AES and RSA Using Chaos
Chaos has attracted much attention in the field of cryptography It describes a system
which is sensitive to initial condition It generates apparently random behavior but at the
same time is completely deterministic Chaos function is used to increase the complexity
and Security of the SystemAES and RSA are the two cryptographic algorithms In AES
we apply the Chaos on S-box where as in RSA we mix the plaintext with Chaos sequence
First then apply for encryption and decryption After Implementing AES and RSA they
compare both the technique on the basis of speed
Advantages
1Chaos function is used to improve complexity
2The speed has been improved with combined technique of AES and RSA along with
chaos technique
12
7) Pahrul Irfan Yudi Prayudi Imam Riadi ( August 2015) ldquo Image
Encryption using Combination of Chaotic System and Rivers Shamir
Adleman (RSA)rdquo International Journal of Computer Applications Vol 123
No6
Security and confidentiality of data or information at the present time has
become an important concern Advanced methods for secure transmission storage and
retrieval of digital images are increasingly needed for a number of military medical
homeland security and other applications Various kinds of techniques for increase
security data or information already is developed one common way is by cryptographic
techniques Cryptography is science to maintain the security of the message by changing
data or information into a different form so the message cannot be recognized To
compensate for increasing computing speeds increases it takes more than one encryption
algorithm to improve security of digital images One way is by using algorithms to
double cryptography do encryption and decryption Cryptographic algorithm often used
today and the proven strength specially the digital image is Algorithm with Chaos
system To improve security at the image then we use Additional algorithms namely
Rivers algorithm Shamir Adleman (RSA) which known as the standard of cryptography
algorithms This research aims to optimize security bitmap image format by combining
the two algorithms namely Chaos-based algorithms and RSA algorithm into one
application Experiments conducted show that the proposed algorithm possesses robust
security features such as fairly uniform distribution high sensitivity to both keys and
plain images almost ideal entropy and the ability to highly de-correlate adjacent pixels
in the cipher images Furthermore it has a large key space and transform image to pure
text file which greatly increases its security for image encryption
Advantages
1 It aims to optimize security bitmap image format by combining the two algorithms
namely Chaos-based algorithms and RSA algorithm into one application
13
8) Weiming Zhang KedeMa NenghaiYu(2013) ldquo Reversibility improved
data hiding in encrypted imagesrdquo School of Information Science and
Technology
A novel reversible data hiding technique in encrypted images is presented in this
paper Instead of embedding data in encrypted images directly some pixels are estimated
before encryption so that additional data can be embedded in the estimating errors A
bench mark encryption algorithm (eg AES) is applied to the rest pixels of the image and
a special encryption scheme is designed to encrypt the estimating errors Without the
encryption key one cannot get access to the original image However provided with the
data hiding key only he can embed in or extract from the encrypted image additional data
without knowledge about the original image Moreover the data extraction and image
recovery are free of errors for all images Experiments demonstrate the feasibility and
efficiency of the proposed method especially in aspect of embedding rate versus Peak
Signal-to-Noise Ratio (PSNR)
The paper proposes a novel method to significantly improve the performance by
reversing the order of encryption and vacating room In the light of this idea we empty
out room prior to image encryption by shifting the histogram of estimating errors of some
pixels and the emptied out room will be used for data hiding The proposed method is
composed of four primary steps vacating room and encrypting image data hiding in the
encrypted image data extraction and image recovery Two different schemes extraction
before decryption and decryption before extraction are raised to cope with different
applications
Advantages
(i) Achieves excellent performance in three aspects complete reversibility PSNR
under given embedding rate separability between data higher extraction and
image decryption
14
CHAPTER 3
PROPOSED METHODOLOGY
The proposed data hiding scheme aims at the security of the hidden data
Embedding is performed in spatial domain The data to be embedded is converted into
binary form from ASCII code using chaos encryption and is embedded into the cover
image obtained after 2D logistic map This embedded image is secured using symmetric
key (K1)They are converted into DNA sequence to provide additional level of security
The hidden data can be extracted from the cover image accurately with the help of
decryption techniques and secret key (K1) The cover image can also be extracted
without any distortion The fig 31 shows the workflow
Fig 31 Work Flow Diagram
SECRET DATA
COVER IMAGE
CHAOTIC
ENCRYPTION
ENCRY 2D LOGISTIC
ENCRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
CHAOTIC
DECRYPTION
ENCRY
SECRET DATA
COVER IMAGE 2D LOGISTIC
DECRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
15
31 Chaotic Encryption
Chaotic cryptography is the application of the mathematical chaos theory to the
practice of the cryptography the study or techniques used to privately and securely
transmit information with the presence of an third-party or adversary The use of chaos
or randomness in cryptography has long been sought after by entities wanting a new way
to encrypt messages However because of the lack of thorough provable security
properties and low acceptable performance chaotic cryptography has encountered
setbacksIn order to use chaos theory acceptably in cryptography they must first be
mapped to each other Properties in chaotic systems and cryptographic primitives share
unique characteristics that allow for the chaotic systems to be applied to cryptography If
chaotic parameters as well as cryptographic keys can be mapped symmetrically or
mapped to produce acceptable and functional outputs it will make it next to impossible
for an adversary to find the outputs without any knowledge the initial values Since
chaotic maps in a real life scenario require a set of numbers that are limited they may in
fact have no real purpose in a cryptosystem if the chaotic behavior can be predicted To
counter this possibility there exists simple to advanced ciphers Chaos theory used in
cryptosystems for commercial implementation has proven to be unsuccessful mainly
because a chaos theories‟ requirement to use intervals of real numbers Given enough
resources and time an adversary could be able to predict functional outcomes Since
chaotic cryptosystems have no root in number theory this would make it difficult or
impossible to implement therefore impractical
32 The RSA Algorithm
The RSA cryptosystem named after its inventors R Rivest A Shamir and L
Adleman is the most widely used public key Cryptosystem It may be used to provide
both secrecy and digital signatures and its security is based on the intractability of the
integer factorizationThe RSA algorithm involves three steps key generation encryption
and decryption
16
321 Key Generation
RSA involves a public key and a private key The public key can be known to
everyone and is used for encrypting messages Messages encrypted with the public key
can only be decrypted in a reasonable amount of time using the private key The keys for
the RSA algorithm are generated the following way To generate the two keys choose
two random large prime numbers p and q For maximum security choose p and q of
equal length Then randomly choose the encryption key e such that e and ( p minus1) (q minus1)
are relatively prime Finally use the extended Euclidean algorithm to compute the
decryption key d such that
d= e-1
mod ( (p-1) (q-1))
Note that d and n are also relatively prime The numbers e and K are the public
key the number d is the private key The two primes p and q are no longer needed They
should be discarded but never revealed
322 Encryption
Firstly receiver transmits her public key (n e) to sender and keeps the private key
secret If sender wishes to send message M to receiver Sender change the message M in
to integer m such that 0 le mltn Then sender computes the cipher text c corresponding to
Cequiv me
(mod n)
323 Decryption
Receiver can recover M from c by using private key exponent d via computing
M equiv cd
(mod n)
17
Algorithm
1Select any two prime numbers say (pq)
2Compute n=pq and also compute empty(119899)=(p-1)(q-1)
3Choose e such that 1ltelt empty(119899)
4Choose d such that (de)mod empty(119899)=1
5Public key is (en) and Private key is (dn)
6 If egt=2 then check i==1 if so return 1 else return 0
7In a iteration check for e(i)==1 if so take mod function of message with n
8Message is converted to cipher text in ASCII form with the key generated
9The cipher data in ASCII form is converted to binary form
33 2D Logistic Encryption
The chaotic system is a deterministic nonlinear system It possesses a varied
characteristics such as high sensitivity to initial conditions and system parameters
random-like behaviors and so forth Chaotic sequences produced by chaotic maps are
pseudo-random sequences their structures are very complex and difficult to be analyzed
and predicted In other words chaotic systems can improve the security of encryption
systems Thus it is advisable to encrypt digital image with chaotic systems There are
two chaotic maps one is logistic map and the other is 2D logistic map In the proposed
work 2D logistic map is used
Logistic map is an example for chaotic map and it is described as follows
x(n+1)=μ x(n)(1minusx(n))
μ is a positive constant sometimes known as the biotic potential gives the so-called
logistic map x(n) is series of values used to plot Logistic mapwhere μ isin [04] x(n) isin
(01) and n = 01 2hellip The research result shows that the system is in chaotic state
under the condition that 356994 lt μ le 4
18
2D logistic map is described in as follows
119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2
119910119894+1 = 1205832 lowast 119910119894(1 minus 119910119894) + 1205732((119909119894)2 + 119909119894 lowast 119910119894)
Where z(xy) is the logistic map 1205831 and 1205832 are positive constant sometimes known as the
biotic potential gives the so-called logistic map x be the position of images in x axis
and y be the position of images in y axis 1205731 and 1205732 are the correlation constantsWhen
275 lt μ1 le 34 275 lt μ2 le 345 015 lt 1205731le 021 and 013 lt 1205732 le 015 the system is in
chaotic state and can generate two chaotic sequences in the region (01] Due to the
system parameter γ1 and γ2 which have smaller value range we set γ1 = 017 and γ2 =
014 other parameters can be seen as secret keys
Algorithm
1A random key is generated in binary form ( 1times256) and it is stored in a array
2The random key generated is translated to map format using block processing (4times4)
3 The row and column wise transformation is carried out
4The key is now used to encrypt the cover image
52D logistic image undergoes substitution and permutation (column and row wise
shuffling is done)
34 DNA Sequence
A single DNA sequence is made up of four nucleic acid
bases A (adenine) C (cytosine) G (guanine) and T (thymine) where A and T are
complements and C and G are complements Let binary number 0 and 1 be
complements so 00 and 11 are complements and 01 and 10 are complements Thus we
can use these four bases A T G and C to encode 01 10 00 and 11 respectively The
encoding method still satisfies the Watson-Crick complement rule Usually each pixel
value of the 8 bit grey image can be expressed to 8 bits binary stream The binary stream
can be encoded to a DNA sequence whose length is 4 For example if the first pixel
19
value of the original image is 75 convert it into a binary stream [01001011] By using the
above DNA encoding rule to encode the stream we can get a DNA sequence [AGTC]
whereas we use A T G and C to express 01 10 00 and 11 respectively We can get a
binary sequence [01001011]
35 Attacks
The steganographic algorithm is used to embed secret messages into cover
image To obtain stego image while exchanging these stego-image through the public
communication channel various attacks have been made The are generally classified
into two types intentional or unintentional attacks Examples of unintentional attacks are
transmission errors lossy compression and changing the visual properties of the stego
document Intentional attacks on the other hand are deliberate attempts to distinguish
stego-objects from unmodified objects and thus detect the presence of covert
communication Attack methods generally exploit the fact that embedding information
usually changes the statistical properties of the objects compared to typical unmodified
objects In this proposed algorithm various attacks have been applied on the encrypted
image They are as follows
1)Shearing
2) Image Scaling
3) Image Rotating
4) Image color reduction
5) Image blurred
6) Image flip
7) cropping and intensity transformation
8) Image sharpening
9) Gaussian Noise and filtering
10) Image Contrast
11) Speckle Noise and Filtering
20
351 Shearing
The image is resized with the scale factor Resized image is rotated with
angle(theta)Finally spatial transformation from control point pairs is implemented
For example
Scale factor 09
Theta10
Fig 32 Shearing Image
352 Image Scaling
It resizes the image with a scale factor and rotation is performed It rotates the
image by angle (degrees) in a counterclockwise direction around its center point To
rotate the image clockwise specify a negative value for angle It makes the output image
large enough to contain the entire rotated image It uses nearest neighbour interpolation
setting the values of pixels in Output image that are outside the rotated image to 0 (zero)
For example
Scale Factor07
Theta30
scaling Image
21
Fig 33 Scaling Image
353 Rotation
It rotates the image by angle degrees in a counterclockwise direction around its
center point To rotate the image clockwise specify a negative value for angle It makes
the output image large enough to contain the entire rotated image It uses nearest
neighbour interpolation setting the values of pixels in Output image that are outside the
rotated image to 0 (zero)
For Example
Theta180
Fig 34 Rotation Image
354 Colour Reduced Image
It creates an indexed image approximation of the RGB image in the array RGB by
dithering the colors in the colormap map The colormap cannot have more than 65536
resized and rotated image
Rotated image
22
colors
For Example
Indexed image with 32 Colors
Fig 35 Colour Reduced Image
355 Blur Image
The image is blurred by using N-D filtering of multidimensional images It filters
the multidimensional array of original image with the multidimensional filter The array
of original image can be logical or a nonsparse numeric array of any class and dimension
The result image has the same size and class as of original image
Fig 36 Blur Image
Color reduced image
50 100 150 200 250 300 350 400 450 500
50
100
150
200
250
300
350
400
450
500
Blurred image
23
356 Flipped Image
It flips the image upside down Flipping is used to invert the image
Fig 37 Flipped Image
357 Cropped Image
It creates an interactive crop image tool associated with the image displayed in the
current figure called the target image The crop image tool is a movable resizable
rectangle that you can position interactively using the mouse When the crop image tool
is active the pointer changes to cross hairs when it is moved over the target image
Using the mouse image to be cropped can be specified by clicking and dragging the
mouse The crop rectangle using the mouse can be moved or resized When sizing and
positioning of the crop rectangle is finished create the cropped image by double-clicking
the left mouse button or by choosing crop image from the context menu Image cropping
returns the cropped image
Flipped image
24
Fig 38 Cropped Image
358 Intensity Transformation Adjust
It maps the intensity values in grayscale image to new values in resultant image
such that 1 of data is saturated at low and high intensity of original image This
increases the contrast of the output image
Fig 39 Intensity Transformation Image
Cropped Image
Intensity Transformation
25
359 Sharpening
Input array values outside the bounds of the array are assumed to equal the nearest
array border value The image is sharpened by using N-D filtering of multidimensional
images It filters the multidimensional array of original image with the multidimensional
filter The array of original image can be logical or a nonsparse numeric array of any
class and dimension The result image has the same size and class as of original image
Fig 310 Sharpened Image
3510 Gaussian Noise and Median Filtering
It adds Gaussian noise to the images Gaussian white noise have constant mean
and variance The noise added image is filtered using Median Filtering Median filtering
is a nonlinear operation often used in image processing to reduce salt and pepper noise
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Sharpened Image
26
Fig 311 Gaussian Noise and Median Filter Image
3511 Histogram of contrast image
It enhances the contrast of images by transforming the values in an intensity
image or the values in the colormap of an indexed image so that the histogram of the
output image approximately matches a specified histogram
Fig 312 Contrast Image
Gaussian Noise
Median Filtering
Contrast Image
27
Fig 313 Histogram of Contrast Image
3512 Speckle noise and Median Filtering
It adds multiplicative noise to the image I using the equation J = I+nI where n is
uniformly distributed random noise with mean 0 and variance v The default for v is 004
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Fig 314 Speckle Noise and Median Filter Image
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Histogram of Contrast Image
0 50 100 150 200 250
Speckle Noise
Median Filtering
28
36 Proposed Algorithm
Step1 Enter two keys a private key and a public key through which the RSA algorithm is
performed
Step2These key are used for encrypting the secret data using chaotic algorithm with
threshold of 2
Step3The secret data is converted into binary format from ASCII code
Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied
Step5The encryption is carried out with the key generated randomly in binary(1times256)
Step6 The 2D logistic substitution and permutation are carried out
Step7The resulting binary sequence is added with the encrypted text in LSB
Step8The image is converted to DNA sequence and transmitted
Step9 Various Attacks have been applied on the resultant image
Step10The inverse process is carried out to retrieve the original image and data
Step11The Performance Metrics have been calculated
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
ACKNOWLEDGEMENT
First I would like to express my praise and gratitude to the Lord who has
showered his grace and blessings enabling me to complete this project in an excellent
manner
I express my sincere thanks to the management of Kumaraguru College of
Technology and Joint Correspondent Shri Shankar Vanavarayar for his kind support
and for providing necessary facilities to carry out the work
I would like to express my sincere thanks to our beloved Principal DrRSKumar
PhD Kumaraguru College of Technology who encouraged me with his valuable
thoughts
I would like to thank DrAVasuki PhD Head of the Department Electronics
and Communication Engineering for her kind support and for providing necessary
facilities to carry out the project work
I wish to thank with everlasting gratitude to the project coordinator
DrMAlagumeenaakshi PhD Asst Professor(SRG) Department of Electronics and
Communication Engineering throughout the course of this project work
I am greatly privileged to express my heartfelt thanks to my project guide
DrAAmsaveni PhD Associate Professor Department of Electronics and
Communication Engineering for her expert counseling and guidance to make this project
to a great deal of success and I wish to convey my deep sense of gratitude to all teaching
and non-teaching staffs of ECE Department for their help and cooperation
Finally I thank my parents and my family members for giving me the moral
support and abundant blessings in all of my activities and my dear friends who helped me
to endure my difficult times with their unfailing support and warm wishes
iii
ABSTRACT
Data hiding is a technique used in the field of information security Using this
technique secret data can be embedded inside a cover medium by the sender and the
secret data and cover medium can be extracted without any distortion by the receiver The
main benefit of this technique is that the cover medium used for embedding can also be
recovered with high quality Data hiding has a wide range of applications such as medical
image sharing multimedia archive management image transcoding video error
concealment and military application According to the problems of steganography the
main effort is to provide a better imperceptibility of stego-image that can be done by
decreasing distortion of image
The proposed method provides a data hiding technique based on Chaotic and 2D
Logistic encryption The cover image is divided into a number of blocks and 2D logistic
map is created The data to be embedded is encrypted using chaos encryption technique
Finding the best location to hide the secret data is an important task so that it will conceal
the existence of the message The optimal location to hide the secret data is embedding
on the LSB bits This results in a stego image which is not only good in quality but is also
able to sustain certain noise After embedding the secret data into cover medium the
symmetric key is applied The resultant binary pixels are converted into DNA sequence
for additional level of security Attacks have been applied on the image The reverse
process is done to convert the stego image into original image and data can also be
extracted This proposed method ensures the three essential properties which are
commonly used to determine quality of data hiding scheme They are imperceptibility
robustness reversibility and security
The performance metrics like Peak Signal to Noise Ratio (PSNR) Mean Square Error
(MSE) Average Difference (AD) Structural Content (SC) Laplacian Mean Squared
Error (LMSE) Normalized Absolute Error (NAE) and Normalized Correlation
Coefficient (NCC) have been evaluated
iv
TABLE OF CONTENTS
CHAPTER NO TITLE PAGE NO
ABSTRACT iv
LIST OF FIGURES vii
LIST OF TABLES viii
LIST OF ABBRIVEATION viii
1 INTRODUCTION 1
11Cryptography 1
12 Steganography 3
13 Reversible Data Hiding 5
2 LITERATURE SURVEY 6
3 PROPOSED METHODOLOGY 14
31 Chaotic Encryption 15
32 The RSA Algorithm 15
321 Key Generation 16
322 Encryption 16
323 Decryption 16
33 2D Logistic Encryption 17
34 DNA Sequence 18
35 Attacks 19
351 Shearing 20
352 Image Scaling 20
353 Rotation 21
354 Colour Reduced Image 21
355 Blur Image 22
356 Flipped Image 23
357 Cropped Image 23
358 Intensity Transformation Image 24
359 Sharpening 25
3510 Gaussian Noise and Median Filtering 25
3511 Histogram of Contrast Image 26
3512 Speckle Noise and Median Filtering 27
36 Proposed Algorithm 28
4 RESULTS AND DISCUSSIONS 295
5 CONCLUSION AND FUTURE WORK 35
51 Conclusion 35
52 Future Work 35
REFERENCES 36
LIST OF PUBLICATIONS 38
vi
LIST OF TABLES
PAGE NO
41 Performance Metric Calculation 33
42 Performance Metric Calculation between original and 34 recovered Barbara image
LIST OF ABBREVIATIONS
2D 2 Dimensional
AD Average Difference
BER Bit Error Rate
LMSE Laplacian Mean Square Error
LSB Least Significant Bit
MD Maximum Difference
MSE Mean Square Error
NCC Normalized Cross Correlation
PSNR Peak Signal to Noise Ratio
SC Structural Content
viii
TABLE TITLE
NO
2
vii
LIST OF FIGURES
FIGURE
NO
CAPTION
PAGE
NO
11 Symmetric-key cryptography 2
12 Public key Cryptography 2
13 Categories of Image Steganography 4
14 Reversible Data Hiding System 5
31 Work Flow Diagram 14
32 Shearing Image 20
33 Scaling Image 20
34 Rotation image 21
35 Colour Reduced Image 22
36 Blur Image 22
37 Flipped Image 23
38 Cropped Image 24
39 Intensity Transformation Image 24
310 Sharpened Image 25
311 Gaussian Noise and Median Filter Image 26
312 Contrast Image 26
313 Histogram of Contrast Image 27
314 Speckle Noise and Median Filter Image 27
41 Gray Scale Cover image of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
31
42 Input Image and 2D Logistic Encrypted Image 32
43 DNA Sequence 32
44 Recovered Image 33
44 Recovered Text 33
2
1
CHAPTER 1
INTRODUCTION
In an information sharing environment security of information plays an important
role Some information that is sensitive or confidential in nature must be kept private
With the introduction of computers the need for automated tools for protecting files and
other information stored in the computer become evident Transmission of sensitive
information via an open internet channel increases the risk of interception There are
many techniques proposed to deal with this issue They are
1) Cryptography
2) Steganography
3) Reversible Data Hiding
11 CRYPTOGRAPHY
Cryptography is the practice and study of techniques for secure communication in
the presence of third parties (called adversaries) More generally it is about constructing
and analyzing protocols that overcome the influence of adversaries This technique alters
the form of the message at the sender and transmits it At the receiver the original
message is extracted It mainly involves 2 operations
Encryption It is the process of the conversion of information from a readable state to
apparent nonsense with the usage of a key It is done by the sender
Decryption It is the reverse process of encryption That is it is the process of converting
scrambled message into the original one with the help of key The key may be similar to
the one which is used in encryption or it may be a different one It is done at the receiver
side
The cryptography is characterized by 3 independent dimensions
2
1) The type of operations used for transforming Plaintext to Cipher text
All encryption algorithms are based on two general principles They are
substitution and transposition Substitution is the one in which each element in the plain
text is transformed into another element Transposition is the one in which elements in
the plain text are rearranged The fundamental condition is that no information be lost
2) The Number of keys used
Based on this we can classify the techniques into two
a) Symmetric-key Cryptography Symmetric-key cryptography refers to encryption
methods in which both the sender and receiver share the same key (or less commonly in
which their keys are different but related in an easily computable way)
Figure 11 Symmetric-key cryptography
b) Public key Cryptography In public-key cryptosystems the public key may be freely
distributed while its paired private key must remain secret In a public-key encryption
system the public key is used for encryption while the private or secret key is used for
decryption
Figure 12 Public key Cryptography
3
3) The way in which the plaintext is processed
There are 2 types
a) Block Cipher It processes the input one block of elements at a time producing an
output block for each input block
b) Stream Cipher It processes the input elements continuously producing output one
element at a time as it goes along
12 STEGANOGRAPHY
It is the art and science of encoding hidden messages in such a way that no one
apart from the sender and intended recipient suspects the existence of the message It is a
form of security through obscurity Generally the hidden messages will appear to be (or
be part of) something else images articles shopping lists or some other cover texts
Plainly visible encrypted messages no matter how unbreakable will arouse interest and
may in themselves be incriminating in countries where encryption is illegal For example
the hidden message may be in invisible ink between the visible lines of a private letter
The advantage of steganography over cryptography alone is that the intended secret
message does not attract attention to itself as an object of scrutiny So cryptography is the
practice of protecting the contents of a message alone steganography is concerned with
concealing the fact that a secret message is being sent as well as concealing the contents
of the message Steganography includes the concealment of information within computer
files In digital steganography electronic communications may include steganographic
coding inside of a transport layer such as a document file image file program or
protocol Media files are ideal for steganographic transmission because of their large size
There has been a rapid growth of interest in steganography for two main reasons
(i) The publishing and broadcasting industries have become interested in techniques for
hiding encrypted copyright marks and serial numbers in digital films audio
recordings books and multimedia products
(ii) Moves by various governments to restrict the availability of encryption services
have motivated people to study methods by which private messages can be
4
embedded in seemingly innocuous cover messages
Fig 13 Categories of Image Steganography
There are many applications for digital steganography of image including
copyright protection feature tagging and secret communication Copyright notice or
watermark can embedded inside an image to identify it as intellectual property If
someone attempts to use this image without permission we can prove by extracting the
watermark In feature tagging captions annotations time stamps and other descriptive
elements can be embedded inside an image Copying the stegondashimage also copies of the
embedded features and only parties who posses the decoding stego-key will be able to
extract and view the features On the other hand secret communication does not advertise
a covert communication by using steganography Therefore it can avoid scrutiny of the
sender message and recipient This is effective only if the hidden communication is not
detected by the others people In general steganography is two types reversible and
irreversible
5
13 Reversible Data Hiding
Figure 14 Reversible Data Hiding System
Secret Message The secret message or information to hide
Cover File Digital Medium The data or medium which concealed the secret message
Stego File A modified version of cover that contains the secret message
Key Additional secret data that is needed for the embedding and extracting processes
and must be known to both the sender and the recipient
Steganographic Method A steganographic function that takes cover secret message
and key as parameters and produces stego as output
Inverse of Steganographic Method A steganographic function that has stego and key
as parameters and produces secret message as output This is the inverse of method used
in embeding process in the sense that the result of the extracting process is identical to the
input of the embedding process
6
CHAPTER 2
LITERATURE SURVEY
1) Nidhi Sethi Deepika Sharma (2014) ldquoA Novel Method Of Image
Encryption using Logistic Mappingrdquo International Journal of Computer
Science Engineering (IJCSE)
This paper presents a new method to develop secure image-encryption techniques
using a logistics based encryption algorithm In this technique a Haar wavelet transform
was used to decompose the image and decorrelate its pixels into averaging and
differencing components The logistic based encryption algorithm produces a cipher of
the test image that has good diffusion and confusion properties The remaining
components (the differencing components) are compressed using a wavelet transform
Many test images are used to demonstrate the validity of the proposed algorithm The
results of several experiments show that the proposed algorithm for image cryptosystems
provides an efficient and secure approach to real-time image encryption and transmission
To send the keys in secure form steganography will be used Steganographic techniques
allow one party to communicate information to another party without a third party even
knowing that the communication is occurring
Advantages
(i) Efficient approach
(ii) Secure key transmission
(iii) Better image quality
7
2) Jing Wang Guoping Jiang (2013) ldquoA Self-Adaptive Parallel Encryption
Algorithm Based on Discrete 2D-Logistic Maprdquo International Journal of
Modern Nonlinear Theory and Application
A self-adaptive parallel encryption algorithm based on discrete 2D-Logistic
map is developed according to the position scrambling and diffusion of multi-direction in
variable space of spatial chaos The binary sequences b1b2b3bn are obtained according
to the user key in which the binary sequence 0 and 1 denote distribution mode of
processors and the number of binary sequence n denotes cycle number Then the
pseudorandom 2D matrix is generated by 2D-Logistic map and adaptive segmentation is
applied in image matrix and pseudorandom matrix according to the value and the number
of binary sequence The parallel operation is used among blocks to improve efficiency
and meet real-time demand in transmission processes However the pixel permutation is
applied in partitioned matrix through ergodic matrix generated by pseudo-random matrix-
block to decrease the correlation of adjacent pixels Then the pixel substitution is used for
fully diffusing through cipher block chaining mode until n cycles The proposed
algorithm can meet the three requirements of parallel operation in image encryption and
the real-time requirement in transmission processes The security is proved by theoretical
analysis and simulation results
Advantages
1Security is provided
2Effeciency is improved
8
3) LJani Anbarasi GSAnandha Mala Modigari Narendra (2014) ldquoDNA
based Multi-Secret Image Sharingrdquo International Conference on
Information and Communication Technologies (ICICT)
Multiple secret sharing algorithm using the YCH scheme combined with
DNA encoding is proposed focusing at better security Firstly DNA encoding for
multiple images is carried out then the addition of these encoded components by DNA is
performed Secondly the (t n) scheme used the Lagrange interpolation polynomial to
share these DNA scrambled matrices is performed bdquot‟ denotes threshold value and bdquon‟
denotes the number of participants The resulting scrambled images are encrypted into n
shares using Shamir‟s polynomial Thirdly these shares are embedded using a modular
operation Finally bdquot‟ or more shares are pooled which reconstructs the scrambled
matrices and by decoding the DNA scrambled matrices multiple secrets are
reconstructed without loss The simulation results and the security analysis prove that this
algorithm is perfect and produces results with better PSNR value The correlation co-
efficient shows that this also has the ability of resisting various attacks
Advantages
1Security is better
2Resistance against Attack
9
4)Della Babya Jitha Thomasa Gisny Augustinea Elsa Georgea Neenu
Rosia Michaela (2014) ldquoA Novel DWT based Image Securing Method using
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Steganography is a data hiding technique that is widely used in various
information securing applications Steganography transmits data by hiding the existence
of the message so that a viewer cannot identify the transmission of message and hence
not able to decrypt it This work proposes a data securing technique that is used for
hiding multiple color images into a single color image using the Discrete Wavelet
Transform The cover image is split up into R G and B planes Secret images are
embedded into these planes An N-level decomposition of the cover image and the secret
images are done and some frequency components of the same are combined Secret
images are then extracted from the stego image Here the stego image obtained has a less
perceptible changes compared to the original image with high overall security
Advantages
1Less perceptible changes
2Overall security is high
10
5)Prasenjit Dasa Subhrajyoti Deba Nirmalya Kara Baby
Bhattacharyaa(2014)ldquoAn Improved DNA based Dual Cover
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Dual cover steganography is an evolving technique in the field of covert
data transmission This paper focuses on the concept of using a theoretical single stranded
DNA (ssDNA) as a primary cover which is extracted from an inconspicuous cover
image They have analyzed the security loopholes and performance issues of the existing
algorithm and proposed an improved algorithm on the same basisthey use 2D Logistic
map for encrypting the cover imageThen overall encryption is RC43 types of encryption
is generally used Performance of both the algorithms are tested against several visual
and statistical attacks and parameterized in terms of both security and capacity The
comparison shows that the proposed improvements provide better overall security
Advantages
1 Robustness against various attack
2 Performance measure are calculated
3 Data hiding improves security
11
6) Bhavana Agrawal Himani Agrawal ( May-2013) ldquoImplementation of AES
and RSA Using Chaos systemrdquo International Journal of Scientific amp
Engineering Research Vol 4 No 5
This paper presents two cryptographic algorithm AES and RSA Using Chaos
Chaos has attracted much attention in the field of cryptography It describes a system
which is sensitive to initial condition It generates apparently random behavior but at the
same time is completely deterministic Chaos function is used to increase the complexity
and Security of the SystemAES and RSA are the two cryptographic algorithms In AES
we apply the Chaos on S-box where as in RSA we mix the plaintext with Chaos sequence
First then apply for encryption and decryption After Implementing AES and RSA they
compare both the technique on the basis of speed
Advantages
1Chaos function is used to improve complexity
2The speed has been improved with combined technique of AES and RSA along with
chaos technique
12
7) Pahrul Irfan Yudi Prayudi Imam Riadi ( August 2015) ldquo Image
Encryption using Combination of Chaotic System and Rivers Shamir
Adleman (RSA)rdquo International Journal of Computer Applications Vol 123
No6
Security and confidentiality of data or information at the present time has
become an important concern Advanced methods for secure transmission storage and
retrieval of digital images are increasingly needed for a number of military medical
homeland security and other applications Various kinds of techniques for increase
security data or information already is developed one common way is by cryptographic
techniques Cryptography is science to maintain the security of the message by changing
data or information into a different form so the message cannot be recognized To
compensate for increasing computing speeds increases it takes more than one encryption
algorithm to improve security of digital images One way is by using algorithms to
double cryptography do encryption and decryption Cryptographic algorithm often used
today and the proven strength specially the digital image is Algorithm with Chaos
system To improve security at the image then we use Additional algorithms namely
Rivers algorithm Shamir Adleman (RSA) which known as the standard of cryptography
algorithms This research aims to optimize security bitmap image format by combining
the two algorithms namely Chaos-based algorithms and RSA algorithm into one
application Experiments conducted show that the proposed algorithm possesses robust
security features such as fairly uniform distribution high sensitivity to both keys and
plain images almost ideal entropy and the ability to highly de-correlate adjacent pixels
in the cipher images Furthermore it has a large key space and transform image to pure
text file which greatly increases its security for image encryption
Advantages
1 It aims to optimize security bitmap image format by combining the two algorithms
namely Chaos-based algorithms and RSA algorithm into one application
13
8) Weiming Zhang KedeMa NenghaiYu(2013) ldquo Reversibility improved
data hiding in encrypted imagesrdquo School of Information Science and
Technology
A novel reversible data hiding technique in encrypted images is presented in this
paper Instead of embedding data in encrypted images directly some pixels are estimated
before encryption so that additional data can be embedded in the estimating errors A
bench mark encryption algorithm (eg AES) is applied to the rest pixels of the image and
a special encryption scheme is designed to encrypt the estimating errors Without the
encryption key one cannot get access to the original image However provided with the
data hiding key only he can embed in or extract from the encrypted image additional data
without knowledge about the original image Moreover the data extraction and image
recovery are free of errors for all images Experiments demonstrate the feasibility and
efficiency of the proposed method especially in aspect of embedding rate versus Peak
Signal-to-Noise Ratio (PSNR)
The paper proposes a novel method to significantly improve the performance by
reversing the order of encryption and vacating room In the light of this idea we empty
out room prior to image encryption by shifting the histogram of estimating errors of some
pixels and the emptied out room will be used for data hiding The proposed method is
composed of four primary steps vacating room and encrypting image data hiding in the
encrypted image data extraction and image recovery Two different schemes extraction
before decryption and decryption before extraction are raised to cope with different
applications
Advantages
(i) Achieves excellent performance in three aspects complete reversibility PSNR
under given embedding rate separability between data higher extraction and
image decryption
14
CHAPTER 3
PROPOSED METHODOLOGY
The proposed data hiding scheme aims at the security of the hidden data
Embedding is performed in spatial domain The data to be embedded is converted into
binary form from ASCII code using chaos encryption and is embedded into the cover
image obtained after 2D logistic map This embedded image is secured using symmetric
key (K1)They are converted into DNA sequence to provide additional level of security
The hidden data can be extracted from the cover image accurately with the help of
decryption techniques and secret key (K1) The cover image can also be extracted
without any distortion The fig 31 shows the workflow
Fig 31 Work Flow Diagram
SECRET DATA
COVER IMAGE
CHAOTIC
ENCRYPTION
ENCRY 2D LOGISTIC
ENCRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
CHAOTIC
DECRYPTION
ENCRY
SECRET DATA
COVER IMAGE 2D LOGISTIC
DECRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
15
31 Chaotic Encryption
Chaotic cryptography is the application of the mathematical chaos theory to the
practice of the cryptography the study or techniques used to privately and securely
transmit information with the presence of an third-party or adversary The use of chaos
or randomness in cryptography has long been sought after by entities wanting a new way
to encrypt messages However because of the lack of thorough provable security
properties and low acceptable performance chaotic cryptography has encountered
setbacksIn order to use chaos theory acceptably in cryptography they must first be
mapped to each other Properties in chaotic systems and cryptographic primitives share
unique characteristics that allow for the chaotic systems to be applied to cryptography If
chaotic parameters as well as cryptographic keys can be mapped symmetrically or
mapped to produce acceptable and functional outputs it will make it next to impossible
for an adversary to find the outputs without any knowledge the initial values Since
chaotic maps in a real life scenario require a set of numbers that are limited they may in
fact have no real purpose in a cryptosystem if the chaotic behavior can be predicted To
counter this possibility there exists simple to advanced ciphers Chaos theory used in
cryptosystems for commercial implementation has proven to be unsuccessful mainly
because a chaos theories‟ requirement to use intervals of real numbers Given enough
resources and time an adversary could be able to predict functional outcomes Since
chaotic cryptosystems have no root in number theory this would make it difficult or
impossible to implement therefore impractical
32 The RSA Algorithm
The RSA cryptosystem named after its inventors R Rivest A Shamir and L
Adleman is the most widely used public key Cryptosystem It may be used to provide
both secrecy and digital signatures and its security is based on the intractability of the
integer factorizationThe RSA algorithm involves three steps key generation encryption
and decryption
16
321 Key Generation
RSA involves a public key and a private key The public key can be known to
everyone and is used for encrypting messages Messages encrypted with the public key
can only be decrypted in a reasonable amount of time using the private key The keys for
the RSA algorithm are generated the following way To generate the two keys choose
two random large prime numbers p and q For maximum security choose p and q of
equal length Then randomly choose the encryption key e such that e and ( p minus1) (q minus1)
are relatively prime Finally use the extended Euclidean algorithm to compute the
decryption key d such that
d= e-1
mod ( (p-1) (q-1))
Note that d and n are also relatively prime The numbers e and K are the public
key the number d is the private key The two primes p and q are no longer needed They
should be discarded but never revealed
322 Encryption
Firstly receiver transmits her public key (n e) to sender and keeps the private key
secret If sender wishes to send message M to receiver Sender change the message M in
to integer m such that 0 le mltn Then sender computes the cipher text c corresponding to
Cequiv me
(mod n)
323 Decryption
Receiver can recover M from c by using private key exponent d via computing
M equiv cd
(mod n)
17
Algorithm
1Select any two prime numbers say (pq)
2Compute n=pq and also compute empty(119899)=(p-1)(q-1)
3Choose e such that 1ltelt empty(119899)
4Choose d such that (de)mod empty(119899)=1
5Public key is (en) and Private key is (dn)
6 If egt=2 then check i==1 if so return 1 else return 0
7In a iteration check for e(i)==1 if so take mod function of message with n
8Message is converted to cipher text in ASCII form with the key generated
9The cipher data in ASCII form is converted to binary form
33 2D Logistic Encryption
The chaotic system is a deterministic nonlinear system It possesses a varied
characteristics such as high sensitivity to initial conditions and system parameters
random-like behaviors and so forth Chaotic sequences produced by chaotic maps are
pseudo-random sequences their structures are very complex and difficult to be analyzed
and predicted In other words chaotic systems can improve the security of encryption
systems Thus it is advisable to encrypt digital image with chaotic systems There are
two chaotic maps one is logistic map and the other is 2D logistic map In the proposed
work 2D logistic map is used
Logistic map is an example for chaotic map and it is described as follows
x(n+1)=μ x(n)(1minusx(n))
μ is a positive constant sometimes known as the biotic potential gives the so-called
logistic map x(n) is series of values used to plot Logistic mapwhere μ isin [04] x(n) isin
(01) and n = 01 2hellip The research result shows that the system is in chaotic state
under the condition that 356994 lt μ le 4
18
2D logistic map is described in as follows
119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2
119910119894+1 = 1205832 lowast 119910119894(1 minus 119910119894) + 1205732((119909119894)2 + 119909119894 lowast 119910119894)
Where z(xy) is the logistic map 1205831 and 1205832 are positive constant sometimes known as the
biotic potential gives the so-called logistic map x be the position of images in x axis
and y be the position of images in y axis 1205731 and 1205732 are the correlation constantsWhen
275 lt μ1 le 34 275 lt μ2 le 345 015 lt 1205731le 021 and 013 lt 1205732 le 015 the system is in
chaotic state and can generate two chaotic sequences in the region (01] Due to the
system parameter γ1 and γ2 which have smaller value range we set γ1 = 017 and γ2 =
014 other parameters can be seen as secret keys
Algorithm
1A random key is generated in binary form ( 1times256) and it is stored in a array
2The random key generated is translated to map format using block processing (4times4)
3 The row and column wise transformation is carried out
4The key is now used to encrypt the cover image
52D logistic image undergoes substitution and permutation (column and row wise
shuffling is done)
34 DNA Sequence
A single DNA sequence is made up of four nucleic acid
bases A (adenine) C (cytosine) G (guanine) and T (thymine) where A and T are
complements and C and G are complements Let binary number 0 and 1 be
complements so 00 and 11 are complements and 01 and 10 are complements Thus we
can use these four bases A T G and C to encode 01 10 00 and 11 respectively The
encoding method still satisfies the Watson-Crick complement rule Usually each pixel
value of the 8 bit grey image can be expressed to 8 bits binary stream The binary stream
can be encoded to a DNA sequence whose length is 4 For example if the first pixel
19
value of the original image is 75 convert it into a binary stream [01001011] By using the
above DNA encoding rule to encode the stream we can get a DNA sequence [AGTC]
whereas we use A T G and C to express 01 10 00 and 11 respectively We can get a
binary sequence [01001011]
35 Attacks
The steganographic algorithm is used to embed secret messages into cover
image To obtain stego image while exchanging these stego-image through the public
communication channel various attacks have been made The are generally classified
into two types intentional or unintentional attacks Examples of unintentional attacks are
transmission errors lossy compression and changing the visual properties of the stego
document Intentional attacks on the other hand are deliberate attempts to distinguish
stego-objects from unmodified objects and thus detect the presence of covert
communication Attack methods generally exploit the fact that embedding information
usually changes the statistical properties of the objects compared to typical unmodified
objects In this proposed algorithm various attacks have been applied on the encrypted
image They are as follows
1)Shearing
2) Image Scaling
3) Image Rotating
4) Image color reduction
5) Image blurred
6) Image flip
7) cropping and intensity transformation
8) Image sharpening
9) Gaussian Noise and filtering
10) Image Contrast
11) Speckle Noise and Filtering
20
351 Shearing
The image is resized with the scale factor Resized image is rotated with
angle(theta)Finally spatial transformation from control point pairs is implemented
For example
Scale factor 09
Theta10
Fig 32 Shearing Image
352 Image Scaling
It resizes the image with a scale factor and rotation is performed It rotates the
image by angle (degrees) in a counterclockwise direction around its center point To
rotate the image clockwise specify a negative value for angle It makes the output image
large enough to contain the entire rotated image It uses nearest neighbour interpolation
setting the values of pixels in Output image that are outside the rotated image to 0 (zero)
For example
Scale Factor07
Theta30
scaling Image
21
Fig 33 Scaling Image
353 Rotation
It rotates the image by angle degrees in a counterclockwise direction around its
center point To rotate the image clockwise specify a negative value for angle It makes
the output image large enough to contain the entire rotated image It uses nearest
neighbour interpolation setting the values of pixels in Output image that are outside the
rotated image to 0 (zero)
For Example
Theta180
Fig 34 Rotation Image
354 Colour Reduced Image
It creates an indexed image approximation of the RGB image in the array RGB by
dithering the colors in the colormap map The colormap cannot have more than 65536
resized and rotated image
Rotated image
22
colors
For Example
Indexed image with 32 Colors
Fig 35 Colour Reduced Image
355 Blur Image
The image is blurred by using N-D filtering of multidimensional images It filters
the multidimensional array of original image with the multidimensional filter The array
of original image can be logical or a nonsparse numeric array of any class and dimension
The result image has the same size and class as of original image
Fig 36 Blur Image
Color reduced image
50 100 150 200 250 300 350 400 450 500
50
100
150
200
250
300
350
400
450
500
Blurred image
23
356 Flipped Image
It flips the image upside down Flipping is used to invert the image
Fig 37 Flipped Image
357 Cropped Image
It creates an interactive crop image tool associated with the image displayed in the
current figure called the target image The crop image tool is a movable resizable
rectangle that you can position interactively using the mouse When the crop image tool
is active the pointer changes to cross hairs when it is moved over the target image
Using the mouse image to be cropped can be specified by clicking and dragging the
mouse The crop rectangle using the mouse can be moved or resized When sizing and
positioning of the crop rectangle is finished create the cropped image by double-clicking
the left mouse button or by choosing crop image from the context menu Image cropping
returns the cropped image
Flipped image
24
Fig 38 Cropped Image
358 Intensity Transformation Adjust
It maps the intensity values in grayscale image to new values in resultant image
such that 1 of data is saturated at low and high intensity of original image This
increases the contrast of the output image
Fig 39 Intensity Transformation Image
Cropped Image
Intensity Transformation
25
359 Sharpening
Input array values outside the bounds of the array are assumed to equal the nearest
array border value The image is sharpened by using N-D filtering of multidimensional
images It filters the multidimensional array of original image with the multidimensional
filter The array of original image can be logical or a nonsparse numeric array of any
class and dimension The result image has the same size and class as of original image
Fig 310 Sharpened Image
3510 Gaussian Noise and Median Filtering
It adds Gaussian noise to the images Gaussian white noise have constant mean
and variance The noise added image is filtered using Median Filtering Median filtering
is a nonlinear operation often used in image processing to reduce salt and pepper noise
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Sharpened Image
26
Fig 311 Gaussian Noise and Median Filter Image
3511 Histogram of contrast image
It enhances the contrast of images by transforming the values in an intensity
image or the values in the colormap of an indexed image so that the histogram of the
output image approximately matches a specified histogram
Fig 312 Contrast Image
Gaussian Noise
Median Filtering
Contrast Image
27
Fig 313 Histogram of Contrast Image
3512 Speckle noise and Median Filtering
It adds multiplicative noise to the image I using the equation J = I+nI where n is
uniformly distributed random noise with mean 0 and variance v The default for v is 004
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Fig 314 Speckle Noise and Median Filter Image
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Histogram of Contrast Image
0 50 100 150 200 250
Speckle Noise
Median Filtering
28
36 Proposed Algorithm
Step1 Enter two keys a private key and a public key through which the RSA algorithm is
performed
Step2These key are used for encrypting the secret data using chaotic algorithm with
threshold of 2
Step3The secret data is converted into binary format from ASCII code
Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied
Step5The encryption is carried out with the key generated randomly in binary(1times256)
Step6 The 2D logistic substitution and permutation are carried out
Step7The resulting binary sequence is added with the encrypted text in LSB
Step8The image is converted to DNA sequence and transmitted
Step9 Various Attacks have been applied on the resultant image
Step10The inverse process is carried out to retrieve the original image and data
Step11The Performance Metrics have been calculated
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
ABSTRACT
Data hiding is a technique used in the field of information security Using this
technique secret data can be embedded inside a cover medium by the sender and the
secret data and cover medium can be extracted without any distortion by the receiver The
main benefit of this technique is that the cover medium used for embedding can also be
recovered with high quality Data hiding has a wide range of applications such as medical
image sharing multimedia archive management image transcoding video error
concealment and military application According to the problems of steganography the
main effort is to provide a better imperceptibility of stego-image that can be done by
decreasing distortion of image
The proposed method provides a data hiding technique based on Chaotic and 2D
Logistic encryption The cover image is divided into a number of blocks and 2D logistic
map is created The data to be embedded is encrypted using chaos encryption technique
Finding the best location to hide the secret data is an important task so that it will conceal
the existence of the message The optimal location to hide the secret data is embedding
on the LSB bits This results in a stego image which is not only good in quality but is also
able to sustain certain noise After embedding the secret data into cover medium the
symmetric key is applied The resultant binary pixels are converted into DNA sequence
for additional level of security Attacks have been applied on the image The reverse
process is done to convert the stego image into original image and data can also be
extracted This proposed method ensures the three essential properties which are
commonly used to determine quality of data hiding scheme They are imperceptibility
robustness reversibility and security
The performance metrics like Peak Signal to Noise Ratio (PSNR) Mean Square Error
(MSE) Average Difference (AD) Structural Content (SC) Laplacian Mean Squared
Error (LMSE) Normalized Absolute Error (NAE) and Normalized Correlation
Coefficient (NCC) have been evaluated
iv
TABLE OF CONTENTS
CHAPTER NO TITLE PAGE NO
ABSTRACT iv
LIST OF FIGURES vii
LIST OF TABLES viii
LIST OF ABBRIVEATION viii
1 INTRODUCTION 1
11Cryptography 1
12 Steganography 3
13 Reversible Data Hiding 5
2 LITERATURE SURVEY 6
3 PROPOSED METHODOLOGY 14
31 Chaotic Encryption 15
32 The RSA Algorithm 15
321 Key Generation 16
322 Encryption 16
323 Decryption 16
33 2D Logistic Encryption 17
34 DNA Sequence 18
35 Attacks 19
351 Shearing 20
352 Image Scaling 20
353 Rotation 21
354 Colour Reduced Image 21
355 Blur Image 22
356 Flipped Image 23
357 Cropped Image 23
358 Intensity Transformation Image 24
359 Sharpening 25
3510 Gaussian Noise and Median Filtering 25
3511 Histogram of Contrast Image 26
3512 Speckle Noise and Median Filtering 27
36 Proposed Algorithm 28
4 RESULTS AND DISCUSSIONS 295
5 CONCLUSION AND FUTURE WORK 35
51 Conclusion 35
52 Future Work 35
REFERENCES 36
LIST OF PUBLICATIONS 38
vi
LIST OF TABLES
PAGE NO
41 Performance Metric Calculation 33
42 Performance Metric Calculation between original and 34 recovered Barbara image
LIST OF ABBREVIATIONS
2D 2 Dimensional
AD Average Difference
BER Bit Error Rate
LMSE Laplacian Mean Square Error
LSB Least Significant Bit
MD Maximum Difference
MSE Mean Square Error
NCC Normalized Cross Correlation
PSNR Peak Signal to Noise Ratio
SC Structural Content
viii
TABLE TITLE
NO
2
vii
LIST OF FIGURES
FIGURE
NO
CAPTION
PAGE
NO
11 Symmetric-key cryptography 2
12 Public key Cryptography 2
13 Categories of Image Steganography 4
14 Reversible Data Hiding System 5
31 Work Flow Diagram 14
32 Shearing Image 20
33 Scaling Image 20
34 Rotation image 21
35 Colour Reduced Image 22
36 Blur Image 22
37 Flipped Image 23
38 Cropped Image 24
39 Intensity Transformation Image 24
310 Sharpened Image 25
311 Gaussian Noise and Median Filter Image 26
312 Contrast Image 26
313 Histogram of Contrast Image 27
314 Speckle Noise and Median Filter Image 27
41 Gray Scale Cover image of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
31
42 Input Image and 2D Logistic Encrypted Image 32
43 DNA Sequence 32
44 Recovered Image 33
44 Recovered Text 33
2
1
CHAPTER 1
INTRODUCTION
In an information sharing environment security of information plays an important
role Some information that is sensitive or confidential in nature must be kept private
With the introduction of computers the need for automated tools for protecting files and
other information stored in the computer become evident Transmission of sensitive
information via an open internet channel increases the risk of interception There are
many techniques proposed to deal with this issue They are
1) Cryptography
2) Steganography
3) Reversible Data Hiding
11 CRYPTOGRAPHY
Cryptography is the practice and study of techniques for secure communication in
the presence of third parties (called adversaries) More generally it is about constructing
and analyzing protocols that overcome the influence of adversaries This technique alters
the form of the message at the sender and transmits it At the receiver the original
message is extracted It mainly involves 2 operations
Encryption It is the process of the conversion of information from a readable state to
apparent nonsense with the usage of a key It is done by the sender
Decryption It is the reverse process of encryption That is it is the process of converting
scrambled message into the original one with the help of key The key may be similar to
the one which is used in encryption or it may be a different one It is done at the receiver
side
The cryptography is characterized by 3 independent dimensions
2
1) The type of operations used for transforming Plaintext to Cipher text
All encryption algorithms are based on two general principles They are
substitution and transposition Substitution is the one in which each element in the plain
text is transformed into another element Transposition is the one in which elements in
the plain text are rearranged The fundamental condition is that no information be lost
2) The Number of keys used
Based on this we can classify the techniques into two
a) Symmetric-key Cryptography Symmetric-key cryptography refers to encryption
methods in which both the sender and receiver share the same key (or less commonly in
which their keys are different but related in an easily computable way)
Figure 11 Symmetric-key cryptography
b) Public key Cryptography In public-key cryptosystems the public key may be freely
distributed while its paired private key must remain secret In a public-key encryption
system the public key is used for encryption while the private or secret key is used for
decryption
Figure 12 Public key Cryptography
3
3) The way in which the plaintext is processed
There are 2 types
a) Block Cipher It processes the input one block of elements at a time producing an
output block for each input block
b) Stream Cipher It processes the input elements continuously producing output one
element at a time as it goes along
12 STEGANOGRAPHY
It is the art and science of encoding hidden messages in such a way that no one
apart from the sender and intended recipient suspects the existence of the message It is a
form of security through obscurity Generally the hidden messages will appear to be (or
be part of) something else images articles shopping lists or some other cover texts
Plainly visible encrypted messages no matter how unbreakable will arouse interest and
may in themselves be incriminating in countries where encryption is illegal For example
the hidden message may be in invisible ink between the visible lines of a private letter
The advantage of steganography over cryptography alone is that the intended secret
message does not attract attention to itself as an object of scrutiny So cryptography is the
practice of protecting the contents of a message alone steganography is concerned with
concealing the fact that a secret message is being sent as well as concealing the contents
of the message Steganography includes the concealment of information within computer
files In digital steganography electronic communications may include steganographic
coding inside of a transport layer such as a document file image file program or
protocol Media files are ideal for steganographic transmission because of their large size
There has been a rapid growth of interest in steganography for two main reasons
(i) The publishing and broadcasting industries have become interested in techniques for
hiding encrypted copyright marks and serial numbers in digital films audio
recordings books and multimedia products
(ii) Moves by various governments to restrict the availability of encryption services
have motivated people to study methods by which private messages can be
4
embedded in seemingly innocuous cover messages
Fig 13 Categories of Image Steganography
There are many applications for digital steganography of image including
copyright protection feature tagging and secret communication Copyright notice or
watermark can embedded inside an image to identify it as intellectual property If
someone attempts to use this image without permission we can prove by extracting the
watermark In feature tagging captions annotations time stamps and other descriptive
elements can be embedded inside an image Copying the stegondashimage also copies of the
embedded features and only parties who posses the decoding stego-key will be able to
extract and view the features On the other hand secret communication does not advertise
a covert communication by using steganography Therefore it can avoid scrutiny of the
sender message and recipient This is effective only if the hidden communication is not
detected by the others people In general steganography is two types reversible and
irreversible
5
13 Reversible Data Hiding
Figure 14 Reversible Data Hiding System
Secret Message The secret message or information to hide
Cover File Digital Medium The data or medium which concealed the secret message
Stego File A modified version of cover that contains the secret message
Key Additional secret data that is needed for the embedding and extracting processes
and must be known to both the sender and the recipient
Steganographic Method A steganographic function that takes cover secret message
and key as parameters and produces stego as output
Inverse of Steganographic Method A steganographic function that has stego and key
as parameters and produces secret message as output This is the inverse of method used
in embeding process in the sense that the result of the extracting process is identical to the
input of the embedding process
6
CHAPTER 2
LITERATURE SURVEY
1) Nidhi Sethi Deepika Sharma (2014) ldquoA Novel Method Of Image
Encryption using Logistic Mappingrdquo International Journal of Computer
Science Engineering (IJCSE)
This paper presents a new method to develop secure image-encryption techniques
using a logistics based encryption algorithm In this technique a Haar wavelet transform
was used to decompose the image and decorrelate its pixels into averaging and
differencing components The logistic based encryption algorithm produces a cipher of
the test image that has good diffusion and confusion properties The remaining
components (the differencing components) are compressed using a wavelet transform
Many test images are used to demonstrate the validity of the proposed algorithm The
results of several experiments show that the proposed algorithm for image cryptosystems
provides an efficient and secure approach to real-time image encryption and transmission
To send the keys in secure form steganography will be used Steganographic techniques
allow one party to communicate information to another party without a third party even
knowing that the communication is occurring
Advantages
(i) Efficient approach
(ii) Secure key transmission
(iii) Better image quality
7
2) Jing Wang Guoping Jiang (2013) ldquoA Self-Adaptive Parallel Encryption
Algorithm Based on Discrete 2D-Logistic Maprdquo International Journal of
Modern Nonlinear Theory and Application
A self-adaptive parallel encryption algorithm based on discrete 2D-Logistic
map is developed according to the position scrambling and diffusion of multi-direction in
variable space of spatial chaos The binary sequences b1b2b3bn are obtained according
to the user key in which the binary sequence 0 and 1 denote distribution mode of
processors and the number of binary sequence n denotes cycle number Then the
pseudorandom 2D matrix is generated by 2D-Logistic map and adaptive segmentation is
applied in image matrix and pseudorandom matrix according to the value and the number
of binary sequence The parallel operation is used among blocks to improve efficiency
and meet real-time demand in transmission processes However the pixel permutation is
applied in partitioned matrix through ergodic matrix generated by pseudo-random matrix-
block to decrease the correlation of adjacent pixels Then the pixel substitution is used for
fully diffusing through cipher block chaining mode until n cycles The proposed
algorithm can meet the three requirements of parallel operation in image encryption and
the real-time requirement in transmission processes The security is proved by theoretical
analysis and simulation results
Advantages
1Security is provided
2Effeciency is improved
8
3) LJani Anbarasi GSAnandha Mala Modigari Narendra (2014) ldquoDNA
based Multi-Secret Image Sharingrdquo International Conference on
Information and Communication Technologies (ICICT)
Multiple secret sharing algorithm using the YCH scheme combined with
DNA encoding is proposed focusing at better security Firstly DNA encoding for
multiple images is carried out then the addition of these encoded components by DNA is
performed Secondly the (t n) scheme used the Lagrange interpolation polynomial to
share these DNA scrambled matrices is performed bdquot‟ denotes threshold value and bdquon‟
denotes the number of participants The resulting scrambled images are encrypted into n
shares using Shamir‟s polynomial Thirdly these shares are embedded using a modular
operation Finally bdquot‟ or more shares are pooled which reconstructs the scrambled
matrices and by decoding the DNA scrambled matrices multiple secrets are
reconstructed without loss The simulation results and the security analysis prove that this
algorithm is perfect and produces results with better PSNR value The correlation co-
efficient shows that this also has the ability of resisting various attacks
Advantages
1Security is better
2Resistance against Attack
9
4)Della Babya Jitha Thomasa Gisny Augustinea Elsa Georgea Neenu
Rosia Michaela (2014) ldquoA Novel DWT based Image Securing Method using
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Steganography is a data hiding technique that is widely used in various
information securing applications Steganography transmits data by hiding the existence
of the message so that a viewer cannot identify the transmission of message and hence
not able to decrypt it This work proposes a data securing technique that is used for
hiding multiple color images into a single color image using the Discrete Wavelet
Transform The cover image is split up into R G and B planes Secret images are
embedded into these planes An N-level decomposition of the cover image and the secret
images are done and some frequency components of the same are combined Secret
images are then extracted from the stego image Here the stego image obtained has a less
perceptible changes compared to the original image with high overall security
Advantages
1Less perceptible changes
2Overall security is high
10
5)Prasenjit Dasa Subhrajyoti Deba Nirmalya Kara Baby
Bhattacharyaa(2014)ldquoAn Improved DNA based Dual Cover
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Dual cover steganography is an evolving technique in the field of covert
data transmission This paper focuses on the concept of using a theoretical single stranded
DNA (ssDNA) as a primary cover which is extracted from an inconspicuous cover
image They have analyzed the security loopholes and performance issues of the existing
algorithm and proposed an improved algorithm on the same basisthey use 2D Logistic
map for encrypting the cover imageThen overall encryption is RC43 types of encryption
is generally used Performance of both the algorithms are tested against several visual
and statistical attacks and parameterized in terms of both security and capacity The
comparison shows that the proposed improvements provide better overall security
Advantages
1 Robustness against various attack
2 Performance measure are calculated
3 Data hiding improves security
11
6) Bhavana Agrawal Himani Agrawal ( May-2013) ldquoImplementation of AES
and RSA Using Chaos systemrdquo International Journal of Scientific amp
Engineering Research Vol 4 No 5
This paper presents two cryptographic algorithm AES and RSA Using Chaos
Chaos has attracted much attention in the field of cryptography It describes a system
which is sensitive to initial condition It generates apparently random behavior but at the
same time is completely deterministic Chaos function is used to increase the complexity
and Security of the SystemAES and RSA are the two cryptographic algorithms In AES
we apply the Chaos on S-box where as in RSA we mix the plaintext with Chaos sequence
First then apply for encryption and decryption After Implementing AES and RSA they
compare both the technique on the basis of speed
Advantages
1Chaos function is used to improve complexity
2The speed has been improved with combined technique of AES and RSA along with
chaos technique
12
7) Pahrul Irfan Yudi Prayudi Imam Riadi ( August 2015) ldquo Image
Encryption using Combination of Chaotic System and Rivers Shamir
Adleman (RSA)rdquo International Journal of Computer Applications Vol 123
No6
Security and confidentiality of data or information at the present time has
become an important concern Advanced methods for secure transmission storage and
retrieval of digital images are increasingly needed for a number of military medical
homeland security and other applications Various kinds of techniques for increase
security data or information already is developed one common way is by cryptographic
techniques Cryptography is science to maintain the security of the message by changing
data or information into a different form so the message cannot be recognized To
compensate for increasing computing speeds increases it takes more than one encryption
algorithm to improve security of digital images One way is by using algorithms to
double cryptography do encryption and decryption Cryptographic algorithm often used
today and the proven strength specially the digital image is Algorithm with Chaos
system To improve security at the image then we use Additional algorithms namely
Rivers algorithm Shamir Adleman (RSA) which known as the standard of cryptography
algorithms This research aims to optimize security bitmap image format by combining
the two algorithms namely Chaos-based algorithms and RSA algorithm into one
application Experiments conducted show that the proposed algorithm possesses robust
security features such as fairly uniform distribution high sensitivity to both keys and
plain images almost ideal entropy and the ability to highly de-correlate adjacent pixels
in the cipher images Furthermore it has a large key space and transform image to pure
text file which greatly increases its security for image encryption
Advantages
1 It aims to optimize security bitmap image format by combining the two algorithms
namely Chaos-based algorithms and RSA algorithm into one application
13
8) Weiming Zhang KedeMa NenghaiYu(2013) ldquo Reversibility improved
data hiding in encrypted imagesrdquo School of Information Science and
Technology
A novel reversible data hiding technique in encrypted images is presented in this
paper Instead of embedding data in encrypted images directly some pixels are estimated
before encryption so that additional data can be embedded in the estimating errors A
bench mark encryption algorithm (eg AES) is applied to the rest pixels of the image and
a special encryption scheme is designed to encrypt the estimating errors Without the
encryption key one cannot get access to the original image However provided with the
data hiding key only he can embed in or extract from the encrypted image additional data
without knowledge about the original image Moreover the data extraction and image
recovery are free of errors for all images Experiments demonstrate the feasibility and
efficiency of the proposed method especially in aspect of embedding rate versus Peak
Signal-to-Noise Ratio (PSNR)
The paper proposes a novel method to significantly improve the performance by
reversing the order of encryption and vacating room In the light of this idea we empty
out room prior to image encryption by shifting the histogram of estimating errors of some
pixels and the emptied out room will be used for data hiding The proposed method is
composed of four primary steps vacating room and encrypting image data hiding in the
encrypted image data extraction and image recovery Two different schemes extraction
before decryption and decryption before extraction are raised to cope with different
applications
Advantages
(i) Achieves excellent performance in three aspects complete reversibility PSNR
under given embedding rate separability between data higher extraction and
image decryption
14
CHAPTER 3
PROPOSED METHODOLOGY
The proposed data hiding scheme aims at the security of the hidden data
Embedding is performed in spatial domain The data to be embedded is converted into
binary form from ASCII code using chaos encryption and is embedded into the cover
image obtained after 2D logistic map This embedded image is secured using symmetric
key (K1)They are converted into DNA sequence to provide additional level of security
The hidden data can be extracted from the cover image accurately with the help of
decryption techniques and secret key (K1) The cover image can also be extracted
without any distortion The fig 31 shows the workflow
Fig 31 Work Flow Diagram
SECRET DATA
COVER IMAGE
CHAOTIC
ENCRYPTION
ENCRY 2D LOGISTIC
ENCRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
CHAOTIC
DECRYPTION
ENCRY
SECRET DATA
COVER IMAGE 2D LOGISTIC
DECRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
15
31 Chaotic Encryption
Chaotic cryptography is the application of the mathematical chaos theory to the
practice of the cryptography the study or techniques used to privately and securely
transmit information with the presence of an third-party or adversary The use of chaos
or randomness in cryptography has long been sought after by entities wanting a new way
to encrypt messages However because of the lack of thorough provable security
properties and low acceptable performance chaotic cryptography has encountered
setbacksIn order to use chaos theory acceptably in cryptography they must first be
mapped to each other Properties in chaotic systems and cryptographic primitives share
unique characteristics that allow for the chaotic systems to be applied to cryptography If
chaotic parameters as well as cryptographic keys can be mapped symmetrically or
mapped to produce acceptable and functional outputs it will make it next to impossible
for an adversary to find the outputs without any knowledge the initial values Since
chaotic maps in a real life scenario require a set of numbers that are limited they may in
fact have no real purpose in a cryptosystem if the chaotic behavior can be predicted To
counter this possibility there exists simple to advanced ciphers Chaos theory used in
cryptosystems for commercial implementation has proven to be unsuccessful mainly
because a chaos theories‟ requirement to use intervals of real numbers Given enough
resources and time an adversary could be able to predict functional outcomes Since
chaotic cryptosystems have no root in number theory this would make it difficult or
impossible to implement therefore impractical
32 The RSA Algorithm
The RSA cryptosystem named after its inventors R Rivest A Shamir and L
Adleman is the most widely used public key Cryptosystem It may be used to provide
both secrecy and digital signatures and its security is based on the intractability of the
integer factorizationThe RSA algorithm involves three steps key generation encryption
and decryption
16
321 Key Generation
RSA involves a public key and a private key The public key can be known to
everyone and is used for encrypting messages Messages encrypted with the public key
can only be decrypted in a reasonable amount of time using the private key The keys for
the RSA algorithm are generated the following way To generate the two keys choose
two random large prime numbers p and q For maximum security choose p and q of
equal length Then randomly choose the encryption key e such that e and ( p minus1) (q minus1)
are relatively prime Finally use the extended Euclidean algorithm to compute the
decryption key d such that
d= e-1
mod ( (p-1) (q-1))
Note that d and n are also relatively prime The numbers e and K are the public
key the number d is the private key The two primes p and q are no longer needed They
should be discarded but never revealed
322 Encryption
Firstly receiver transmits her public key (n e) to sender and keeps the private key
secret If sender wishes to send message M to receiver Sender change the message M in
to integer m such that 0 le mltn Then sender computes the cipher text c corresponding to
Cequiv me
(mod n)
323 Decryption
Receiver can recover M from c by using private key exponent d via computing
M equiv cd
(mod n)
17
Algorithm
1Select any two prime numbers say (pq)
2Compute n=pq and also compute empty(119899)=(p-1)(q-1)
3Choose e such that 1ltelt empty(119899)
4Choose d such that (de)mod empty(119899)=1
5Public key is (en) and Private key is (dn)
6 If egt=2 then check i==1 if so return 1 else return 0
7In a iteration check for e(i)==1 if so take mod function of message with n
8Message is converted to cipher text in ASCII form with the key generated
9The cipher data in ASCII form is converted to binary form
33 2D Logistic Encryption
The chaotic system is a deterministic nonlinear system It possesses a varied
characteristics such as high sensitivity to initial conditions and system parameters
random-like behaviors and so forth Chaotic sequences produced by chaotic maps are
pseudo-random sequences their structures are very complex and difficult to be analyzed
and predicted In other words chaotic systems can improve the security of encryption
systems Thus it is advisable to encrypt digital image with chaotic systems There are
two chaotic maps one is logistic map and the other is 2D logistic map In the proposed
work 2D logistic map is used
Logistic map is an example for chaotic map and it is described as follows
x(n+1)=μ x(n)(1minusx(n))
μ is a positive constant sometimes known as the biotic potential gives the so-called
logistic map x(n) is series of values used to plot Logistic mapwhere μ isin [04] x(n) isin
(01) and n = 01 2hellip The research result shows that the system is in chaotic state
under the condition that 356994 lt μ le 4
18
2D logistic map is described in as follows
119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2
119910119894+1 = 1205832 lowast 119910119894(1 minus 119910119894) + 1205732((119909119894)2 + 119909119894 lowast 119910119894)
Where z(xy) is the logistic map 1205831 and 1205832 are positive constant sometimes known as the
biotic potential gives the so-called logistic map x be the position of images in x axis
and y be the position of images in y axis 1205731 and 1205732 are the correlation constantsWhen
275 lt μ1 le 34 275 lt μ2 le 345 015 lt 1205731le 021 and 013 lt 1205732 le 015 the system is in
chaotic state and can generate two chaotic sequences in the region (01] Due to the
system parameter γ1 and γ2 which have smaller value range we set γ1 = 017 and γ2 =
014 other parameters can be seen as secret keys
Algorithm
1A random key is generated in binary form ( 1times256) and it is stored in a array
2The random key generated is translated to map format using block processing (4times4)
3 The row and column wise transformation is carried out
4The key is now used to encrypt the cover image
52D logistic image undergoes substitution and permutation (column and row wise
shuffling is done)
34 DNA Sequence
A single DNA sequence is made up of four nucleic acid
bases A (adenine) C (cytosine) G (guanine) and T (thymine) where A and T are
complements and C and G are complements Let binary number 0 and 1 be
complements so 00 and 11 are complements and 01 and 10 are complements Thus we
can use these four bases A T G and C to encode 01 10 00 and 11 respectively The
encoding method still satisfies the Watson-Crick complement rule Usually each pixel
value of the 8 bit grey image can be expressed to 8 bits binary stream The binary stream
can be encoded to a DNA sequence whose length is 4 For example if the first pixel
19
value of the original image is 75 convert it into a binary stream [01001011] By using the
above DNA encoding rule to encode the stream we can get a DNA sequence [AGTC]
whereas we use A T G and C to express 01 10 00 and 11 respectively We can get a
binary sequence [01001011]
35 Attacks
The steganographic algorithm is used to embed secret messages into cover
image To obtain stego image while exchanging these stego-image through the public
communication channel various attacks have been made The are generally classified
into two types intentional or unintentional attacks Examples of unintentional attacks are
transmission errors lossy compression and changing the visual properties of the stego
document Intentional attacks on the other hand are deliberate attempts to distinguish
stego-objects from unmodified objects and thus detect the presence of covert
communication Attack methods generally exploit the fact that embedding information
usually changes the statistical properties of the objects compared to typical unmodified
objects In this proposed algorithm various attacks have been applied on the encrypted
image They are as follows
1)Shearing
2) Image Scaling
3) Image Rotating
4) Image color reduction
5) Image blurred
6) Image flip
7) cropping and intensity transformation
8) Image sharpening
9) Gaussian Noise and filtering
10) Image Contrast
11) Speckle Noise and Filtering
20
351 Shearing
The image is resized with the scale factor Resized image is rotated with
angle(theta)Finally spatial transformation from control point pairs is implemented
For example
Scale factor 09
Theta10
Fig 32 Shearing Image
352 Image Scaling
It resizes the image with a scale factor and rotation is performed It rotates the
image by angle (degrees) in a counterclockwise direction around its center point To
rotate the image clockwise specify a negative value for angle It makes the output image
large enough to contain the entire rotated image It uses nearest neighbour interpolation
setting the values of pixels in Output image that are outside the rotated image to 0 (zero)
For example
Scale Factor07
Theta30
scaling Image
21
Fig 33 Scaling Image
353 Rotation
It rotates the image by angle degrees in a counterclockwise direction around its
center point To rotate the image clockwise specify a negative value for angle It makes
the output image large enough to contain the entire rotated image It uses nearest
neighbour interpolation setting the values of pixels in Output image that are outside the
rotated image to 0 (zero)
For Example
Theta180
Fig 34 Rotation Image
354 Colour Reduced Image
It creates an indexed image approximation of the RGB image in the array RGB by
dithering the colors in the colormap map The colormap cannot have more than 65536
resized and rotated image
Rotated image
22
colors
For Example
Indexed image with 32 Colors
Fig 35 Colour Reduced Image
355 Blur Image
The image is blurred by using N-D filtering of multidimensional images It filters
the multidimensional array of original image with the multidimensional filter The array
of original image can be logical or a nonsparse numeric array of any class and dimension
The result image has the same size and class as of original image
Fig 36 Blur Image
Color reduced image
50 100 150 200 250 300 350 400 450 500
50
100
150
200
250
300
350
400
450
500
Blurred image
23
356 Flipped Image
It flips the image upside down Flipping is used to invert the image
Fig 37 Flipped Image
357 Cropped Image
It creates an interactive crop image tool associated with the image displayed in the
current figure called the target image The crop image tool is a movable resizable
rectangle that you can position interactively using the mouse When the crop image tool
is active the pointer changes to cross hairs when it is moved over the target image
Using the mouse image to be cropped can be specified by clicking and dragging the
mouse The crop rectangle using the mouse can be moved or resized When sizing and
positioning of the crop rectangle is finished create the cropped image by double-clicking
the left mouse button or by choosing crop image from the context menu Image cropping
returns the cropped image
Flipped image
24
Fig 38 Cropped Image
358 Intensity Transformation Adjust
It maps the intensity values in grayscale image to new values in resultant image
such that 1 of data is saturated at low and high intensity of original image This
increases the contrast of the output image
Fig 39 Intensity Transformation Image
Cropped Image
Intensity Transformation
25
359 Sharpening
Input array values outside the bounds of the array are assumed to equal the nearest
array border value The image is sharpened by using N-D filtering of multidimensional
images It filters the multidimensional array of original image with the multidimensional
filter The array of original image can be logical or a nonsparse numeric array of any
class and dimension The result image has the same size and class as of original image
Fig 310 Sharpened Image
3510 Gaussian Noise and Median Filtering
It adds Gaussian noise to the images Gaussian white noise have constant mean
and variance The noise added image is filtered using Median Filtering Median filtering
is a nonlinear operation often used in image processing to reduce salt and pepper noise
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Sharpened Image
26
Fig 311 Gaussian Noise and Median Filter Image
3511 Histogram of contrast image
It enhances the contrast of images by transforming the values in an intensity
image or the values in the colormap of an indexed image so that the histogram of the
output image approximately matches a specified histogram
Fig 312 Contrast Image
Gaussian Noise
Median Filtering
Contrast Image
27
Fig 313 Histogram of Contrast Image
3512 Speckle noise and Median Filtering
It adds multiplicative noise to the image I using the equation J = I+nI where n is
uniformly distributed random noise with mean 0 and variance v The default for v is 004
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Fig 314 Speckle Noise and Median Filter Image
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Histogram of Contrast Image
0 50 100 150 200 250
Speckle Noise
Median Filtering
28
36 Proposed Algorithm
Step1 Enter two keys a private key and a public key through which the RSA algorithm is
performed
Step2These key are used for encrypting the secret data using chaotic algorithm with
threshold of 2
Step3The secret data is converted into binary format from ASCII code
Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied
Step5The encryption is carried out with the key generated randomly in binary(1times256)
Step6 The 2D logistic substitution and permutation are carried out
Step7The resulting binary sequence is added with the encrypted text in LSB
Step8The image is converted to DNA sequence and transmitted
Step9 Various Attacks have been applied on the resultant image
Step10The inverse process is carried out to retrieve the original image and data
Step11The Performance Metrics have been calculated
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
TABLE OF CONTENTS
CHAPTER NO TITLE PAGE NO
ABSTRACT iv
LIST OF FIGURES vii
LIST OF TABLES viii
LIST OF ABBRIVEATION viii
1 INTRODUCTION 1
11Cryptography 1
12 Steganography 3
13 Reversible Data Hiding 5
2 LITERATURE SURVEY 6
3 PROPOSED METHODOLOGY 14
31 Chaotic Encryption 15
32 The RSA Algorithm 15
321 Key Generation 16
322 Encryption 16
323 Decryption 16
33 2D Logistic Encryption 17
34 DNA Sequence 18
35 Attacks 19
351 Shearing 20
352 Image Scaling 20
353 Rotation 21
354 Colour Reduced Image 21
355 Blur Image 22
356 Flipped Image 23
357 Cropped Image 23
358 Intensity Transformation Image 24
359 Sharpening 25
3510 Gaussian Noise and Median Filtering 25
3511 Histogram of Contrast Image 26
3512 Speckle Noise and Median Filtering 27
36 Proposed Algorithm 28
4 RESULTS AND DISCUSSIONS 295
5 CONCLUSION AND FUTURE WORK 35
51 Conclusion 35
52 Future Work 35
REFERENCES 36
LIST OF PUBLICATIONS 38
vi
LIST OF TABLES
PAGE NO
41 Performance Metric Calculation 33
42 Performance Metric Calculation between original and 34 recovered Barbara image
LIST OF ABBREVIATIONS
2D 2 Dimensional
AD Average Difference
BER Bit Error Rate
LMSE Laplacian Mean Square Error
LSB Least Significant Bit
MD Maximum Difference
MSE Mean Square Error
NCC Normalized Cross Correlation
PSNR Peak Signal to Noise Ratio
SC Structural Content
viii
TABLE TITLE
NO
2
vii
LIST OF FIGURES
FIGURE
NO
CAPTION
PAGE
NO
11 Symmetric-key cryptography 2
12 Public key Cryptography 2
13 Categories of Image Steganography 4
14 Reversible Data Hiding System 5
31 Work Flow Diagram 14
32 Shearing Image 20
33 Scaling Image 20
34 Rotation image 21
35 Colour Reduced Image 22
36 Blur Image 22
37 Flipped Image 23
38 Cropped Image 24
39 Intensity Transformation Image 24
310 Sharpened Image 25
311 Gaussian Noise and Median Filter Image 26
312 Contrast Image 26
313 Histogram of Contrast Image 27
314 Speckle Noise and Median Filter Image 27
41 Gray Scale Cover image of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
31
42 Input Image and 2D Logistic Encrypted Image 32
43 DNA Sequence 32
44 Recovered Image 33
44 Recovered Text 33
2
1
CHAPTER 1
INTRODUCTION
In an information sharing environment security of information plays an important
role Some information that is sensitive or confidential in nature must be kept private
With the introduction of computers the need for automated tools for protecting files and
other information stored in the computer become evident Transmission of sensitive
information via an open internet channel increases the risk of interception There are
many techniques proposed to deal with this issue They are
1) Cryptography
2) Steganography
3) Reversible Data Hiding
11 CRYPTOGRAPHY
Cryptography is the practice and study of techniques for secure communication in
the presence of third parties (called adversaries) More generally it is about constructing
and analyzing protocols that overcome the influence of adversaries This technique alters
the form of the message at the sender and transmits it At the receiver the original
message is extracted It mainly involves 2 operations
Encryption It is the process of the conversion of information from a readable state to
apparent nonsense with the usage of a key It is done by the sender
Decryption It is the reverse process of encryption That is it is the process of converting
scrambled message into the original one with the help of key The key may be similar to
the one which is used in encryption or it may be a different one It is done at the receiver
side
The cryptography is characterized by 3 independent dimensions
2
1) The type of operations used for transforming Plaintext to Cipher text
All encryption algorithms are based on two general principles They are
substitution and transposition Substitution is the one in which each element in the plain
text is transformed into another element Transposition is the one in which elements in
the plain text are rearranged The fundamental condition is that no information be lost
2) The Number of keys used
Based on this we can classify the techniques into two
a) Symmetric-key Cryptography Symmetric-key cryptography refers to encryption
methods in which both the sender and receiver share the same key (or less commonly in
which their keys are different but related in an easily computable way)
Figure 11 Symmetric-key cryptography
b) Public key Cryptography In public-key cryptosystems the public key may be freely
distributed while its paired private key must remain secret In a public-key encryption
system the public key is used for encryption while the private or secret key is used for
decryption
Figure 12 Public key Cryptography
3
3) The way in which the plaintext is processed
There are 2 types
a) Block Cipher It processes the input one block of elements at a time producing an
output block for each input block
b) Stream Cipher It processes the input elements continuously producing output one
element at a time as it goes along
12 STEGANOGRAPHY
It is the art and science of encoding hidden messages in such a way that no one
apart from the sender and intended recipient suspects the existence of the message It is a
form of security through obscurity Generally the hidden messages will appear to be (or
be part of) something else images articles shopping lists or some other cover texts
Plainly visible encrypted messages no matter how unbreakable will arouse interest and
may in themselves be incriminating in countries where encryption is illegal For example
the hidden message may be in invisible ink between the visible lines of a private letter
The advantage of steganography over cryptography alone is that the intended secret
message does not attract attention to itself as an object of scrutiny So cryptography is the
practice of protecting the contents of a message alone steganography is concerned with
concealing the fact that a secret message is being sent as well as concealing the contents
of the message Steganography includes the concealment of information within computer
files In digital steganography electronic communications may include steganographic
coding inside of a transport layer such as a document file image file program or
protocol Media files are ideal for steganographic transmission because of their large size
There has been a rapid growth of interest in steganography for two main reasons
(i) The publishing and broadcasting industries have become interested in techniques for
hiding encrypted copyright marks and serial numbers in digital films audio
recordings books and multimedia products
(ii) Moves by various governments to restrict the availability of encryption services
have motivated people to study methods by which private messages can be
4
embedded in seemingly innocuous cover messages
Fig 13 Categories of Image Steganography
There are many applications for digital steganography of image including
copyright protection feature tagging and secret communication Copyright notice or
watermark can embedded inside an image to identify it as intellectual property If
someone attempts to use this image without permission we can prove by extracting the
watermark In feature tagging captions annotations time stamps and other descriptive
elements can be embedded inside an image Copying the stegondashimage also copies of the
embedded features and only parties who posses the decoding stego-key will be able to
extract and view the features On the other hand secret communication does not advertise
a covert communication by using steganography Therefore it can avoid scrutiny of the
sender message and recipient This is effective only if the hidden communication is not
detected by the others people In general steganography is two types reversible and
irreversible
5
13 Reversible Data Hiding
Figure 14 Reversible Data Hiding System
Secret Message The secret message or information to hide
Cover File Digital Medium The data or medium which concealed the secret message
Stego File A modified version of cover that contains the secret message
Key Additional secret data that is needed for the embedding and extracting processes
and must be known to both the sender and the recipient
Steganographic Method A steganographic function that takes cover secret message
and key as parameters and produces stego as output
Inverse of Steganographic Method A steganographic function that has stego and key
as parameters and produces secret message as output This is the inverse of method used
in embeding process in the sense that the result of the extracting process is identical to the
input of the embedding process
6
CHAPTER 2
LITERATURE SURVEY
1) Nidhi Sethi Deepika Sharma (2014) ldquoA Novel Method Of Image
Encryption using Logistic Mappingrdquo International Journal of Computer
Science Engineering (IJCSE)
This paper presents a new method to develop secure image-encryption techniques
using a logistics based encryption algorithm In this technique a Haar wavelet transform
was used to decompose the image and decorrelate its pixels into averaging and
differencing components The logistic based encryption algorithm produces a cipher of
the test image that has good diffusion and confusion properties The remaining
components (the differencing components) are compressed using a wavelet transform
Many test images are used to demonstrate the validity of the proposed algorithm The
results of several experiments show that the proposed algorithm for image cryptosystems
provides an efficient and secure approach to real-time image encryption and transmission
To send the keys in secure form steganography will be used Steganographic techniques
allow one party to communicate information to another party without a third party even
knowing that the communication is occurring
Advantages
(i) Efficient approach
(ii) Secure key transmission
(iii) Better image quality
7
2) Jing Wang Guoping Jiang (2013) ldquoA Self-Adaptive Parallel Encryption
Algorithm Based on Discrete 2D-Logistic Maprdquo International Journal of
Modern Nonlinear Theory and Application
A self-adaptive parallel encryption algorithm based on discrete 2D-Logistic
map is developed according to the position scrambling and diffusion of multi-direction in
variable space of spatial chaos The binary sequences b1b2b3bn are obtained according
to the user key in which the binary sequence 0 and 1 denote distribution mode of
processors and the number of binary sequence n denotes cycle number Then the
pseudorandom 2D matrix is generated by 2D-Logistic map and adaptive segmentation is
applied in image matrix and pseudorandom matrix according to the value and the number
of binary sequence The parallel operation is used among blocks to improve efficiency
and meet real-time demand in transmission processes However the pixel permutation is
applied in partitioned matrix through ergodic matrix generated by pseudo-random matrix-
block to decrease the correlation of adjacent pixels Then the pixel substitution is used for
fully diffusing through cipher block chaining mode until n cycles The proposed
algorithm can meet the three requirements of parallel operation in image encryption and
the real-time requirement in transmission processes The security is proved by theoretical
analysis and simulation results
Advantages
1Security is provided
2Effeciency is improved
8
3) LJani Anbarasi GSAnandha Mala Modigari Narendra (2014) ldquoDNA
based Multi-Secret Image Sharingrdquo International Conference on
Information and Communication Technologies (ICICT)
Multiple secret sharing algorithm using the YCH scheme combined with
DNA encoding is proposed focusing at better security Firstly DNA encoding for
multiple images is carried out then the addition of these encoded components by DNA is
performed Secondly the (t n) scheme used the Lagrange interpolation polynomial to
share these DNA scrambled matrices is performed bdquot‟ denotes threshold value and bdquon‟
denotes the number of participants The resulting scrambled images are encrypted into n
shares using Shamir‟s polynomial Thirdly these shares are embedded using a modular
operation Finally bdquot‟ or more shares are pooled which reconstructs the scrambled
matrices and by decoding the DNA scrambled matrices multiple secrets are
reconstructed without loss The simulation results and the security analysis prove that this
algorithm is perfect and produces results with better PSNR value The correlation co-
efficient shows that this also has the ability of resisting various attacks
Advantages
1Security is better
2Resistance against Attack
9
4)Della Babya Jitha Thomasa Gisny Augustinea Elsa Georgea Neenu
Rosia Michaela (2014) ldquoA Novel DWT based Image Securing Method using
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Steganography is a data hiding technique that is widely used in various
information securing applications Steganography transmits data by hiding the existence
of the message so that a viewer cannot identify the transmission of message and hence
not able to decrypt it This work proposes a data securing technique that is used for
hiding multiple color images into a single color image using the Discrete Wavelet
Transform The cover image is split up into R G and B planes Secret images are
embedded into these planes An N-level decomposition of the cover image and the secret
images are done and some frequency components of the same are combined Secret
images are then extracted from the stego image Here the stego image obtained has a less
perceptible changes compared to the original image with high overall security
Advantages
1Less perceptible changes
2Overall security is high
10
5)Prasenjit Dasa Subhrajyoti Deba Nirmalya Kara Baby
Bhattacharyaa(2014)ldquoAn Improved DNA based Dual Cover
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Dual cover steganography is an evolving technique in the field of covert
data transmission This paper focuses on the concept of using a theoretical single stranded
DNA (ssDNA) as a primary cover which is extracted from an inconspicuous cover
image They have analyzed the security loopholes and performance issues of the existing
algorithm and proposed an improved algorithm on the same basisthey use 2D Logistic
map for encrypting the cover imageThen overall encryption is RC43 types of encryption
is generally used Performance of both the algorithms are tested against several visual
and statistical attacks and parameterized in terms of both security and capacity The
comparison shows that the proposed improvements provide better overall security
Advantages
1 Robustness against various attack
2 Performance measure are calculated
3 Data hiding improves security
11
6) Bhavana Agrawal Himani Agrawal ( May-2013) ldquoImplementation of AES
and RSA Using Chaos systemrdquo International Journal of Scientific amp
Engineering Research Vol 4 No 5
This paper presents two cryptographic algorithm AES and RSA Using Chaos
Chaos has attracted much attention in the field of cryptography It describes a system
which is sensitive to initial condition It generates apparently random behavior but at the
same time is completely deterministic Chaos function is used to increase the complexity
and Security of the SystemAES and RSA are the two cryptographic algorithms In AES
we apply the Chaos on S-box where as in RSA we mix the plaintext with Chaos sequence
First then apply for encryption and decryption After Implementing AES and RSA they
compare both the technique on the basis of speed
Advantages
1Chaos function is used to improve complexity
2The speed has been improved with combined technique of AES and RSA along with
chaos technique
12
7) Pahrul Irfan Yudi Prayudi Imam Riadi ( August 2015) ldquo Image
Encryption using Combination of Chaotic System and Rivers Shamir
Adleman (RSA)rdquo International Journal of Computer Applications Vol 123
No6
Security and confidentiality of data or information at the present time has
become an important concern Advanced methods for secure transmission storage and
retrieval of digital images are increasingly needed for a number of military medical
homeland security and other applications Various kinds of techniques for increase
security data or information already is developed one common way is by cryptographic
techniques Cryptography is science to maintain the security of the message by changing
data or information into a different form so the message cannot be recognized To
compensate for increasing computing speeds increases it takes more than one encryption
algorithm to improve security of digital images One way is by using algorithms to
double cryptography do encryption and decryption Cryptographic algorithm often used
today and the proven strength specially the digital image is Algorithm with Chaos
system To improve security at the image then we use Additional algorithms namely
Rivers algorithm Shamir Adleman (RSA) which known as the standard of cryptography
algorithms This research aims to optimize security bitmap image format by combining
the two algorithms namely Chaos-based algorithms and RSA algorithm into one
application Experiments conducted show that the proposed algorithm possesses robust
security features such as fairly uniform distribution high sensitivity to both keys and
plain images almost ideal entropy and the ability to highly de-correlate adjacent pixels
in the cipher images Furthermore it has a large key space and transform image to pure
text file which greatly increases its security for image encryption
Advantages
1 It aims to optimize security bitmap image format by combining the two algorithms
namely Chaos-based algorithms and RSA algorithm into one application
13
8) Weiming Zhang KedeMa NenghaiYu(2013) ldquo Reversibility improved
data hiding in encrypted imagesrdquo School of Information Science and
Technology
A novel reversible data hiding technique in encrypted images is presented in this
paper Instead of embedding data in encrypted images directly some pixels are estimated
before encryption so that additional data can be embedded in the estimating errors A
bench mark encryption algorithm (eg AES) is applied to the rest pixels of the image and
a special encryption scheme is designed to encrypt the estimating errors Without the
encryption key one cannot get access to the original image However provided with the
data hiding key only he can embed in or extract from the encrypted image additional data
without knowledge about the original image Moreover the data extraction and image
recovery are free of errors for all images Experiments demonstrate the feasibility and
efficiency of the proposed method especially in aspect of embedding rate versus Peak
Signal-to-Noise Ratio (PSNR)
The paper proposes a novel method to significantly improve the performance by
reversing the order of encryption and vacating room In the light of this idea we empty
out room prior to image encryption by shifting the histogram of estimating errors of some
pixels and the emptied out room will be used for data hiding The proposed method is
composed of four primary steps vacating room and encrypting image data hiding in the
encrypted image data extraction and image recovery Two different schemes extraction
before decryption and decryption before extraction are raised to cope with different
applications
Advantages
(i) Achieves excellent performance in three aspects complete reversibility PSNR
under given embedding rate separability between data higher extraction and
image decryption
14
CHAPTER 3
PROPOSED METHODOLOGY
The proposed data hiding scheme aims at the security of the hidden data
Embedding is performed in spatial domain The data to be embedded is converted into
binary form from ASCII code using chaos encryption and is embedded into the cover
image obtained after 2D logistic map This embedded image is secured using symmetric
key (K1)They are converted into DNA sequence to provide additional level of security
The hidden data can be extracted from the cover image accurately with the help of
decryption techniques and secret key (K1) The cover image can also be extracted
without any distortion The fig 31 shows the workflow
Fig 31 Work Flow Diagram
SECRET DATA
COVER IMAGE
CHAOTIC
ENCRYPTION
ENCRY 2D LOGISTIC
ENCRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
CHAOTIC
DECRYPTION
ENCRY
SECRET DATA
COVER IMAGE 2D LOGISTIC
DECRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
15
31 Chaotic Encryption
Chaotic cryptography is the application of the mathematical chaos theory to the
practice of the cryptography the study or techniques used to privately and securely
transmit information with the presence of an third-party or adversary The use of chaos
or randomness in cryptography has long been sought after by entities wanting a new way
to encrypt messages However because of the lack of thorough provable security
properties and low acceptable performance chaotic cryptography has encountered
setbacksIn order to use chaos theory acceptably in cryptography they must first be
mapped to each other Properties in chaotic systems and cryptographic primitives share
unique characteristics that allow for the chaotic systems to be applied to cryptography If
chaotic parameters as well as cryptographic keys can be mapped symmetrically or
mapped to produce acceptable and functional outputs it will make it next to impossible
for an adversary to find the outputs without any knowledge the initial values Since
chaotic maps in a real life scenario require a set of numbers that are limited they may in
fact have no real purpose in a cryptosystem if the chaotic behavior can be predicted To
counter this possibility there exists simple to advanced ciphers Chaos theory used in
cryptosystems for commercial implementation has proven to be unsuccessful mainly
because a chaos theories‟ requirement to use intervals of real numbers Given enough
resources and time an adversary could be able to predict functional outcomes Since
chaotic cryptosystems have no root in number theory this would make it difficult or
impossible to implement therefore impractical
32 The RSA Algorithm
The RSA cryptosystem named after its inventors R Rivest A Shamir and L
Adleman is the most widely used public key Cryptosystem It may be used to provide
both secrecy and digital signatures and its security is based on the intractability of the
integer factorizationThe RSA algorithm involves three steps key generation encryption
and decryption
16
321 Key Generation
RSA involves a public key and a private key The public key can be known to
everyone and is used for encrypting messages Messages encrypted with the public key
can only be decrypted in a reasonable amount of time using the private key The keys for
the RSA algorithm are generated the following way To generate the two keys choose
two random large prime numbers p and q For maximum security choose p and q of
equal length Then randomly choose the encryption key e such that e and ( p minus1) (q minus1)
are relatively prime Finally use the extended Euclidean algorithm to compute the
decryption key d such that
d= e-1
mod ( (p-1) (q-1))
Note that d and n are also relatively prime The numbers e and K are the public
key the number d is the private key The two primes p and q are no longer needed They
should be discarded but never revealed
322 Encryption
Firstly receiver transmits her public key (n e) to sender and keeps the private key
secret If sender wishes to send message M to receiver Sender change the message M in
to integer m such that 0 le mltn Then sender computes the cipher text c corresponding to
Cequiv me
(mod n)
323 Decryption
Receiver can recover M from c by using private key exponent d via computing
M equiv cd
(mod n)
17
Algorithm
1Select any two prime numbers say (pq)
2Compute n=pq and also compute empty(119899)=(p-1)(q-1)
3Choose e such that 1ltelt empty(119899)
4Choose d such that (de)mod empty(119899)=1
5Public key is (en) and Private key is (dn)
6 If egt=2 then check i==1 if so return 1 else return 0
7In a iteration check for e(i)==1 if so take mod function of message with n
8Message is converted to cipher text in ASCII form with the key generated
9The cipher data in ASCII form is converted to binary form
33 2D Logistic Encryption
The chaotic system is a deterministic nonlinear system It possesses a varied
characteristics such as high sensitivity to initial conditions and system parameters
random-like behaviors and so forth Chaotic sequences produced by chaotic maps are
pseudo-random sequences their structures are very complex and difficult to be analyzed
and predicted In other words chaotic systems can improve the security of encryption
systems Thus it is advisable to encrypt digital image with chaotic systems There are
two chaotic maps one is logistic map and the other is 2D logistic map In the proposed
work 2D logistic map is used
Logistic map is an example for chaotic map and it is described as follows
x(n+1)=μ x(n)(1minusx(n))
μ is a positive constant sometimes known as the biotic potential gives the so-called
logistic map x(n) is series of values used to plot Logistic mapwhere μ isin [04] x(n) isin
(01) and n = 01 2hellip The research result shows that the system is in chaotic state
under the condition that 356994 lt μ le 4
18
2D logistic map is described in as follows
119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2
119910119894+1 = 1205832 lowast 119910119894(1 minus 119910119894) + 1205732((119909119894)2 + 119909119894 lowast 119910119894)
Where z(xy) is the logistic map 1205831 and 1205832 are positive constant sometimes known as the
biotic potential gives the so-called logistic map x be the position of images in x axis
and y be the position of images in y axis 1205731 and 1205732 are the correlation constantsWhen
275 lt μ1 le 34 275 lt μ2 le 345 015 lt 1205731le 021 and 013 lt 1205732 le 015 the system is in
chaotic state and can generate two chaotic sequences in the region (01] Due to the
system parameter γ1 and γ2 which have smaller value range we set γ1 = 017 and γ2 =
014 other parameters can be seen as secret keys
Algorithm
1A random key is generated in binary form ( 1times256) and it is stored in a array
2The random key generated is translated to map format using block processing (4times4)
3 The row and column wise transformation is carried out
4The key is now used to encrypt the cover image
52D logistic image undergoes substitution and permutation (column and row wise
shuffling is done)
34 DNA Sequence
A single DNA sequence is made up of four nucleic acid
bases A (adenine) C (cytosine) G (guanine) and T (thymine) where A and T are
complements and C and G are complements Let binary number 0 and 1 be
complements so 00 and 11 are complements and 01 and 10 are complements Thus we
can use these four bases A T G and C to encode 01 10 00 and 11 respectively The
encoding method still satisfies the Watson-Crick complement rule Usually each pixel
value of the 8 bit grey image can be expressed to 8 bits binary stream The binary stream
can be encoded to a DNA sequence whose length is 4 For example if the first pixel
19
value of the original image is 75 convert it into a binary stream [01001011] By using the
above DNA encoding rule to encode the stream we can get a DNA sequence [AGTC]
whereas we use A T G and C to express 01 10 00 and 11 respectively We can get a
binary sequence [01001011]
35 Attacks
The steganographic algorithm is used to embed secret messages into cover
image To obtain stego image while exchanging these stego-image through the public
communication channel various attacks have been made The are generally classified
into two types intentional or unintentional attacks Examples of unintentional attacks are
transmission errors lossy compression and changing the visual properties of the stego
document Intentional attacks on the other hand are deliberate attempts to distinguish
stego-objects from unmodified objects and thus detect the presence of covert
communication Attack methods generally exploit the fact that embedding information
usually changes the statistical properties of the objects compared to typical unmodified
objects In this proposed algorithm various attacks have been applied on the encrypted
image They are as follows
1)Shearing
2) Image Scaling
3) Image Rotating
4) Image color reduction
5) Image blurred
6) Image flip
7) cropping and intensity transformation
8) Image sharpening
9) Gaussian Noise and filtering
10) Image Contrast
11) Speckle Noise and Filtering
20
351 Shearing
The image is resized with the scale factor Resized image is rotated with
angle(theta)Finally spatial transformation from control point pairs is implemented
For example
Scale factor 09
Theta10
Fig 32 Shearing Image
352 Image Scaling
It resizes the image with a scale factor and rotation is performed It rotates the
image by angle (degrees) in a counterclockwise direction around its center point To
rotate the image clockwise specify a negative value for angle It makes the output image
large enough to contain the entire rotated image It uses nearest neighbour interpolation
setting the values of pixels in Output image that are outside the rotated image to 0 (zero)
For example
Scale Factor07
Theta30
scaling Image
21
Fig 33 Scaling Image
353 Rotation
It rotates the image by angle degrees in a counterclockwise direction around its
center point To rotate the image clockwise specify a negative value for angle It makes
the output image large enough to contain the entire rotated image It uses nearest
neighbour interpolation setting the values of pixels in Output image that are outside the
rotated image to 0 (zero)
For Example
Theta180
Fig 34 Rotation Image
354 Colour Reduced Image
It creates an indexed image approximation of the RGB image in the array RGB by
dithering the colors in the colormap map The colormap cannot have more than 65536
resized and rotated image
Rotated image
22
colors
For Example
Indexed image with 32 Colors
Fig 35 Colour Reduced Image
355 Blur Image
The image is blurred by using N-D filtering of multidimensional images It filters
the multidimensional array of original image with the multidimensional filter The array
of original image can be logical or a nonsparse numeric array of any class and dimension
The result image has the same size and class as of original image
Fig 36 Blur Image
Color reduced image
50 100 150 200 250 300 350 400 450 500
50
100
150
200
250
300
350
400
450
500
Blurred image
23
356 Flipped Image
It flips the image upside down Flipping is used to invert the image
Fig 37 Flipped Image
357 Cropped Image
It creates an interactive crop image tool associated with the image displayed in the
current figure called the target image The crop image tool is a movable resizable
rectangle that you can position interactively using the mouse When the crop image tool
is active the pointer changes to cross hairs when it is moved over the target image
Using the mouse image to be cropped can be specified by clicking and dragging the
mouse The crop rectangle using the mouse can be moved or resized When sizing and
positioning of the crop rectangle is finished create the cropped image by double-clicking
the left mouse button or by choosing crop image from the context menu Image cropping
returns the cropped image
Flipped image
24
Fig 38 Cropped Image
358 Intensity Transformation Adjust
It maps the intensity values in grayscale image to new values in resultant image
such that 1 of data is saturated at low and high intensity of original image This
increases the contrast of the output image
Fig 39 Intensity Transformation Image
Cropped Image
Intensity Transformation
25
359 Sharpening
Input array values outside the bounds of the array are assumed to equal the nearest
array border value The image is sharpened by using N-D filtering of multidimensional
images It filters the multidimensional array of original image with the multidimensional
filter The array of original image can be logical or a nonsparse numeric array of any
class and dimension The result image has the same size and class as of original image
Fig 310 Sharpened Image
3510 Gaussian Noise and Median Filtering
It adds Gaussian noise to the images Gaussian white noise have constant mean
and variance The noise added image is filtered using Median Filtering Median filtering
is a nonlinear operation often used in image processing to reduce salt and pepper noise
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Sharpened Image
26
Fig 311 Gaussian Noise and Median Filter Image
3511 Histogram of contrast image
It enhances the contrast of images by transforming the values in an intensity
image or the values in the colormap of an indexed image so that the histogram of the
output image approximately matches a specified histogram
Fig 312 Contrast Image
Gaussian Noise
Median Filtering
Contrast Image
27
Fig 313 Histogram of Contrast Image
3512 Speckle noise and Median Filtering
It adds multiplicative noise to the image I using the equation J = I+nI where n is
uniformly distributed random noise with mean 0 and variance v The default for v is 004
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Fig 314 Speckle Noise and Median Filter Image
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Histogram of Contrast Image
0 50 100 150 200 250
Speckle Noise
Median Filtering
28
36 Proposed Algorithm
Step1 Enter two keys a private key and a public key through which the RSA algorithm is
performed
Step2These key are used for encrypting the secret data using chaotic algorithm with
threshold of 2
Step3The secret data is converted into binary format from ASCII code
Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied
Step5The encryption is carried out with the key generated randomly in binary(1times256)
Step6 The 2D logistic substitution and permutation are carried out
Step7The resulting binary sequence is added with the encrypted text in LSB
Step8The image is converted to DNA sequence and transmitted
Step9 Various Attacks have been applied on the resultant image
Step10The inverse process is carried out to retrieve the original image and data
Step11The Performance Metrics have been calculated
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
357 Cropped Image 23
358 Intensity Transformation Image 24
359 Sharpening 25
3510 Gaussian Noise and Median Filtering 25
3511 Histogram of Contrast Image 26
3512 Speckle Noise and Median Filtering 27
36 Proposed Algorithm 28
4 RESULTS AND DISCUSSIONS 295
5 CONCLUSION AND FUTURE WORK 35
51 Conclusion 35
52 Future Work 35
REFERENCES 36
LIST OF PUBLICATIONS 38
vi
LIST OF TABLES
PAGE NO
41 Performance Metric Calculation 33
42 Performance Metric Calculation between original and 34 recovered Barbara image
LIST OF ABBREVIATIONS
2D 2 Dimensional
AD Average Difference
BER Bit Error Rate
LMSE Laplacian Mean Square Error
LSB Least Significant Bit
MD Maximum Difference
MSE Mean Square Error
NCC Normalized Cross Correlation
PSNR Peak Signal to Noise Ratio
SC Structural Content
viii
TABLE TITLE
NO
2
vii
LIST OF FIGURES
FIGURE
NO
CAPTION
PAGE
NO
11 Symmetric-key cryptography 2
12 Public key Cryptography 2
13 Categories of Image Steganography 4
14 Reversible Data Hiding System 5
31 Work Flow Diagram 14
32 Shearing Image 20
33 Scaling Image 20
34 Rotation image 21
35 Colour Reduced Image 22
36 Blur Image 22
37 Flipped Image 23
38 Cropped Image 24
39 Intensity Transformation Image 24
310 Sharpened Image 25
311 Gaussian Noise and Median Filter Image 26
312 Contrast Image 26
313 Histogram of Contrast Image 27
314 Speckle Noise and Median Filter Image 27
41 Gray Scale Cover image of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
31
42 Input Image and 2D Logistic Encrypted Image 32
43 DNA Sequence 32
44 Recovered Image 33
44 Recovered Text 33
2
1
CHAPTER 1
INTRODUCTION
In an information sharing environment security of information plays an important
role Some information that is sensitive or confidential in nature must be kept private
With the introduction of computers the need for automated tools for protecting files and
other information stored in the computer become evident Transmission of sensitive
information via an open internet channel increases the risk of interception There are
many techniques proposed to deal with this issue They are
1) Cryptography
2) Steganography
3) Reversible Data Hiding
11 CRYPTOGRAPHY
Cryptography is the practice and study of techniques for secure communication in
the presence of third parties (called adversaries) More generally it is about constructing
and analyzing protocols that overcome the influence of adversaries This technique alters
the form of the message at the sender and transmits it At the receiver the original
message is extracted It mainly involves 2 operations
Encryption It is the process of the conversion of information from a readable state to
apparent nonsense with the usage of a key It is done by the sender
Decryption It is the reverse process of encryption That is it is the process of converting
scrambled message into the original one with the help of key The key may be similar to
the one which is used in encryption or it may be a different one It is done at the receiver
side
The cryptography is characterized by 3 independent dimensions
2
1) The type of operations used for transforming Plaintext to Cipher text
All encryption algorithms are based on two general principles They are
substitution and transposition Substitution is the one in which each element in the plain
text is transformed into another element Transposition is the one in which elements in
the plain text are rearranged The fundamental condition is that no information be lost
2) The Number of keys used
Based on this we can classify the techniques into two
a) Symmetric-key Cryptography Symmetric-key cryptography refers to encryption
methods in which both the sender and receiver share the same key (or less commonly in
which their keys are different but related in an easily computable way)
Figure 11 Symmetric-key cryptography
b) Public key Cryptography In public-key cryptosystems the public key may be freely
distributed while its paired private key must remain secret In a public-key encryption
system the public key is used for encryption while the private or secret key is used for
decryption
Figure 12 Public key Cryptography
3
3) The way in which the plaintext is processed
There are 2 types
a) Block Cipher It processes the input one block of elements at a time producing an
output block for each input block
b) Stream Cipher It processes the input elements continuously producing output one
element at a time as it goes along
12 STEGANOGRAPHY
It is the art and science of encoding hidden messages in such a way that no one
apart from the sender and intended recipient suspects the existence of the message It is a
form of security through obscurity Generally the hidden messages will appear to be (or
be part of) something else images articles shopping lists or some other cover texts
Plainly visible encrypted messages no matter how unbreakable will arouse interest and
may in themselves be incriminating in countries where encryption is illegal For example
the hidden message may be in invisible ink between the visible lines of a private letter
The advantage of steganography over cryptography alone is that the intended secret
message does not attract attention to itself as an object of scrutiny So cryptography is the
practice of protecting the contents of a message alone steganography is concerned with
concealing the fact that a secret message is being sent as well as concealing the contents
of the message Steganography includes the concealment of information within computer
files In digital steganography electronic communications may include steganographic
coding inside of a transport layer such as a document file image file program or
protocol Media files are ideal for steganographic transmission because of their large size
There has been a rapid growth of interest in steganography for two main reasons
(i) The publishing and broadcasting industries have become interested in techniques for
hiding encrypted copyright marks and serial numbers in digital films audio
recordings books and multimedia products
(ii) Moves by various governments to restrict the availability of encryption services
have motivated people to study methods by which private messages can be
4
embedded in seemingly innocuous cover messages
Fig 13 Categories of Image Steganography
There are many applications for digital steganography of image including
copyright protection feature tagging and secret communication Copyright notice or
watermark can embedded inside an image to identify it as intellectual property If
someone attempts to use this image without permission we can prove by extracting the
watermark In feature tagging captions annotations time stamps and other descriptive
elements can be embedded inside an image Copying the stegondashimage also copies of the
embedded features and only parties who posses the decoding stego-key will be able to
extract and view the features On the other hand secret communication does not advertise
a covert communication by using steganography Therefore it can avoid scrutiny of the
sender message and recipient This is effective only if the hidden communication is not
detected by the others people In general steganography is two types reversible and
irreversible
5
13 Reversible Data Hiding
Figure 14 Reversible Data Hiding System
Secret Message The secret message or information to hide
Cover File Digital Medium The data or medium which concealed the secret message
Stego File A modified version of cover that contains the secret message
Key Additional secret data that is needed for the embedding and extracting processes
and must be known to both the sender and the recipient
Steganographic Method A steganographic function that takes cover secret message
and key as parameters and produces stego as output
Inverse of Steganographic Method A steganographic function that has stego and key
as parameters and produces secret message as output This is the inverse of method used
in embeding process in the sense that the result of the extracting process is identical to the
input of the embedding process
6
CHAPTER 2
LITERATURE SURVEY
1) Nidhi Sethi Deepika Sharma (2014) ldquoA Novel Method Of Image
Encryption using Logistic Mappingrdquo International Journal of Computer
Science Engineering (IJCSE)
This paper presents a new method to develop secure image-encryption techniques
using a logistics based encryption algorithm In this technique a Haar wavelet transform
was used to decompose the image and decorrelate its pixels into averaging and
differencing components The logistic based encryption algorithm produces a cipher of
the test image that has good diffusion and confusion properties The remaining
components (the differencing components) are compressed using a wavelet transform
Many test images are used to demonstrate the validity of the proposed algorithm The
results of several experiments show that the proposed algorithm for image cryptosystems
provides an efficient and secure approach to real-time image encryption and transmission
To send the keys in secure form steganography will be used Steganographic techniques
allow one party to communicate information to another party without a third party even
knowing that the communication is occurring
Advantages
(i) Efficient approach
(ii) Secure key transmission
(iii) Better image quality
7
2) Jing Wang Guoping Jiang (2013) ldquoA Self-Adaptive Parallel Encryption
Algorithm Based on Discrete 2D-Logistic Maprdquo International Journal of
Modern Nonlinear Theory and Application
A self-adaptive parallel encryption algorithm based on discrete 2D-Logistic
map is developed according to the position scrambling and diffusion of multi-direction in
variable space of spatial chaos The binary sequences b1b2b3bn are obtained according
to the user key in which the binary sequence 0 and 1 denote distribution mode of
processors and the number of binary sequence n denotes cycle number Then the
pseudorandom 2D matrix is generated by 2D-Logistic map and adaptive segmentation is
applied in image matrix and pseudorandom matrix according to the value and the number
of binary sequence The parallel operation is used among blocks to improve efficiency
and meet real-time demand in transmission processes However the pixel permutation is
applied in partitioned matrix through ergodic matrix generated by pseudo-random matrix-
block to decrease the correlation of adjacent pixels Then the pixel substitution is used for
fully diffusing through cipher block chaining mode until n cycles The proposed
algorithm can meet the three requirements of parallel operation in image encryption and
the real-time requirement in transmission processes The security is proved by theoretical
analysis and simulation results
Advantages
1Security is provided
2Effeciency is improved
8
3) LJani Anbarasi GSAnandha Mala Modigari Narendra (2014) ldquoDNA
based Multi-Secret Image Sharingrdquo International Conference on
Information and Communication Technologies (ICICT)
Multiple secret sharing algorithm using the YCH scheme combined with
DNA encoding is proposed focusing at better security Firstly DNA encoding for
multiple images is carried out then the addition of these encoded components by DNA is
performed Secondly the (t n) scheme used the Lagrange interpolation polynomial to
share these DNA scrambled matrices is performed bdquot‟ denotes threshold value and bdquon‟
denotes the number of participants The resulting scrambled images are encrypted into n
shares using Shamir‟s polynomial Thirdly these shares are embedded using a modular
operation Finally bdquot‟ or more shares are pooled which reconstructs the scrambled
matrices and by decoding the DNA scrambled matrices multiple secrets are
reconstructed without loss The simulation results and the security analysis prove that this
algorithm is perfect and produces results with better PSNR value The correlation co-
efficient shows that this also has the ability of resisting various attacks
Advantages
1Security is better
2Resistance against Attack
9
4)Della Babya Jitha Thomasa Gisny Augustinea Elsa Georgea Neenu
Rosia Michaela (2014) ldquoA Novel DWT based Image Securing Method using
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Steganography is a data hiding technique that is widely used in various
information securing applications Steganography transmits data by hiding the existence
of the message so that a viewer cannot identify the transmission of message and hence
not able to decrypt it This work proposes a data securing technique that is used for
hiding multiple color images into a single color image using the Discrete Wavelet
Transform The cover image is split up into R G and B planes Secret images are
embedded into these planes An N-level decomposition of the cover image and the secret
images are done and some frequency components of the same are combined Secret
images are then extracted from the stego image Here the stego image obtained has a less
perceptible changes compared to the original image with high overall security
Advantages
1Less perceptible changes
2Overall security is high
10
5)Prasenjit Dasa Subhrajyoti Deba Nirmalya Kara Baby
Bhattacharyaa(2014)ldquoAn Improved DNA based Dual Cover
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Dual cover steganography is an evolving technique in the field of covert
data transmission This paper focuses on the concept of using a theoretical single stranded
DNA (ssDNA) as a primary cover which is extracted from an inconspicuous cover
image They have analyzed the security loopholes and performance issues of the existing
algorithm and proposed an improved algorithm on the same basisthey use 2D Logistic
map for encrypting the cover imageThen overall encryption is RC43 types of encryption
is generally used Performance of both the algorithms are tested against several visual
and statistical attacks and parameterized in terms of both security and capacity The
comparison shows that the proposed improvements provide better overall security
Advantages
1 Robustness against various attack
2 Performance measure are calculated
3 Data hiding improves security
11
6) Bhavana Agrawal Himani Agrawal ( May-2013) ldquoImplementation of AES
and RSA Using Chaos systemrdquo International Journal of Scientific amp
Engineering Research Vol 4 No 5
This paper presents two cryptographic algorithm AES and RSA Using Chaos
Chaos has attracted much attention in the field of cryptography It describes a system
which is sensitive to initial condition It generates apparently random behavior but at the
same time is completely deterministic Chaos function is used to increase the complexity
and Security of the SystemAES and RSA are the two cryptographic algorithms In AES
we apply the Chaos on S-box where as in RSA we mix the plaintext with Chaos sequence
First then apply for encryption and decryption After Implementing AES and RSA they
compare both the technique on the basis of speed
Advantages
1Chaos function is used to improve complexity
2The speed has been improved with combined technique of AES and RSA along with
chaos technique
12
7) Pahrul Irfan Yudi Prayudi Imam Riadi ( August 2015) ldquo Image
Encryption using Combination of Chaotic System and Rivers Shamir
Adleman (RSA)rdquo International Journal of Computer Applications Vol 123
No6
Security and confidentiality of data or information at the present time has
become an important concern Advanced methods for secure transmission storage and
retrieval of digital images are increasingly needed for a number of military medical
homeland security and other applications Various kinds of techniques for increase
security data or information already is developed one common way is by cryptographic
techniques Cryptography is science to maintain the security of the message by changing
data or information into a different form so the message cannot be recognized To
compensate for increasing computing speeds increases it takes more than one encryption
algorithm to improve security of digital images One way is by using algorithms to
double cryptography do encryption and decryption Cryptographic algorithm often used
today and the proven strength specially the digital image is Algorithm with Chaos
system To improve security at the image then we use Additional algorithms namely
Rivers algorithm Shamir Adleman (RSA) which known as the standard of cryptography
algorithms This research aims to optimize security bitmap image format by combining
the two algorithms namely Chaos-based algorithms and RSA algorithm into one
application Experiments conducted show that the proposed algorithm possesses robust
security features such as fairly uniform distribution high sensitivity to both keys and
plain images almost ideal entropy and the ability to highly de-correlate adjacent pixels
in the cipher images Furthermore it has a large key space and transform image to pure
text file which greatly increases its security for image encryption
Advantages
1 It aims to optimize security bitmap image format by combining the two algorithms
namely Chaos-based algorithms and RSA algorithm into one application
13
8) Weiming Zhang KedeMa NenghaiYu(2013) ldquo Reversibility improved
data hiding in encrypted imagesrdquo School of Information Science and
Technology
A novel reversible data hiding technique in encrypted images is presented in this
paper Instead of embedding data in encrypted images directly some pixels are estimated
before encryption so that additional data can be embedded in the estimating errors A
bench mark encryption algorithm (eg AES) is applied to the rest pixels of the image and
a special encryption scheme is designed to encrypt the estimating errors Without the
encryption key one cannot get access to the original image However provided with the
data hiding key only he can embed in or extract from the encrypted image additional data
without knowledge about the original image Moreover the data extraction and image
recovery are free of errors for all images Experiments demonstrate the feasibility and
efficiency of the proposed method especially in aspect of embedding rate versus Peak
Signal-to-Noise Ratio (PSNR)
The paper proposes a novel method to significantly improve the performance by
reversing the order of encryption and vacating room In the light of this idea we empty
out room prior to image encryption by shifting the histogram of estimating errors of some
pixels and the emptied out room will be used for data hiding The proposed method is
composed of four primary steps vacating room and encrypting image data hiding in the
encrypted image data extraction and image recovery Two different schemes extraction
before decryption and decryption before extraction are raised to cope with different
applications
Advantages
(i) Achieves excellent performance in three aspects complete reversibility PSNR
under given embedding rate separability between data higher extraction and
image decryption
14
CHAPTER 3
PROPOSED METHODOLOGY
The proposed data hiding scheme aims at the security of the hidden data
Embedding is performed in spatial domain The data to be embedded is converted into
binary form from ASCII code using chaos encryption and is embedded into the cover
image obtained after 2D logistic map This embedded image is secured using symmetric
key (K1)They are converted into DNA sequence to provide additional level of security
The hidden data can be extracted from the cover image accurately with the help of
decryption techniques and secret key (K1) The cover image can also be extracted
without any distortion The fig 31 shows the workflow
Fig 31 Work Flow Diagram
SECRET DATA
COVER IMAGE
CHAOTIC
ENCRYPTION
ENCRY 2D LOGISTIC
ENCRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
CHAOTIC
DECRYPTION
ENCRY
SECRET DATA
COVER IMAGE 2D LOGISTIC
DECRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
15
31 Chaotic Encryption
Chaotic cryptography is the application of the mathematical chaos theory to the
practice of the cryptography the study or techniques used to privately and securely
transmit information with the presence of an third-party or adversary The use of chaos
or randomness in cryptography has long been sought after by entities wanting a new way
to encrypt messages However because of the lack of thorough provable security
properties and low acceptable performance chaotic cryptography has encountered
setbacksIn order to use chaos theory acceptably in cryptography they must first be
mapped to each other Properties in chaotic systems and cryptographic primitives share
unique characteristics that allow for the chaotic systems to be applied to cryptography If
chaotic parameters as well as cryptographic keys can be mapped symmetrically or
mapped to produce acceptable and functional outputs it will make it next to impossible
for an adversary to find the outputs without any knowledge the initial values Since
chaotic maps in a real life scenario require a set of numbers that are limited they may in
fact have no real purpose in a cryptosystem if the chaotic behavior can be predicted To
counter this possibility there exists simple to advanced ciphers Chaos theory used in
cryptosystems for commercial implementation has proven to be unsuccessful mainly
because a chaos theories‟ requirement to use intervals of real numbers Given enough
resources and time an adversary could be able to predict functional outcomes Since
chaotic cryptosystems have no root in number theory this would make it difficult or
impossible to implement therefore impractical
32 The RSA Algorithm
The RSA cryptosystem named after its inventors R Rivest A Shamir and L
Adleman is the most widely used public key Cryptosystem It may be used to provide
both secrecy and digital signatures and its security is based on the intractability of the
integer factorizationThe RSA algorithm involves three steps key generation encryption
and decryption
16
321 Key Generation
RSA involves a public key and a private key The public key can be known to
everyone and is used for encrypting messages Messages encrypted with the public key
can only be decrypted in a reasonable amount of time using the private key The keys for
the RSA algorithm are generated the following way To generate the two keys choose
two random large prime numbers p and q For maximum security choose p and q of
equal length Then randomly choose the encryption key e such that e and ( p minus1) (q minus1)
are relatively prime Finally use the extended Euclidean algorithm to compute the
decryption key d such that
d= e-1
mod ( (p-1) (q-1))
Note that d and n are also relatively prime The numbers e and K are the public
key the number d is the private key The two primes p and q are no longer needed They
should be discarded but never revealed
322 Encryption
Firstly receiver transmits her public key (n e) to sender and keeps the private key
secret If sender wishes to send message M to receiver Sender change the message M in
to integer m such that 0 le mltn Then sender computes the cipher text c corresponding to
Cequiv me
(mod n)
323 Decryption
Receiver can recover M from c by using private key exponent d via computing
M equiv cd
(mod n)
17
Algorithm
1Select any two prime numbers say (pq)
2Compute n=pq and also compute empty(119899)=(p-1)(q-1)
3Choose e such that 1ltelt empty(119899)
4Choose d such that (de)mod empty(119899)=1
5Public key is (en) and Private key is (dn)
6 If egt=2 then check i==1 if so return 1 else return 0
7In a iteration check for e(i)==1 if so take mod function of message with n
8Message is converted to cipher text in ASCII form with the key generated
9The cipher data in ASCII form is converted to binary form
33 2D Logistic Encryption
The chaotic system is a deterministic nonlinear system It possesses a varied
characteristics such as high sensitivity to initial conditions and system parameters
random-like behaviors and so forth Chaotic sequences produced by chaotic maps are
pseudo-random sequences their structures are very complex and difficult to be analyzed
and predicted In other words chaotic systems can improve the security of encryption
systems Thus it is advisable to encrypt digital image with chaotic systems There are
two chaotic maps one is logistic map and the other is 2D logistic map In the proposed
work 2D logistic map is used
Logistic map is an example for chaotic map and it is described as follows
x(n+1)=μ x(n)(1minusx(n))
μ is a positive constant sometimes known as the biotic potential gives the so-called
logistic map x(n) is series of values used to plot Logistic mapwhere μ isin [04] x(n) isin
(01) and n = 01 2hellip The research result shows that the system is in chaotic state
under the condition that 356994 lt μ le 4
18
2D logistic map is described in as follows
119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2
119910119894+1 = 1205832 lowast 119910119894(1 minus 119910119894) + 1205732((119909119894)2 + 119909119894 lowast 119910119894)
Where z(xy) is the logistic map 1205831 and 1205832 are positive constant sometimes known as the
biotic potential gives the so-called logistic map x be the position of images in x axis
and y be the position of images in y axis 1205731 and 1205732 are the correlation constantsWhen
275 lt μ1 le 34 275 lt μ2 le 345 015 lt 1205731le 021 and 013 lt 1205732 le 015 the system is in
chaotic state and can generate two chaotic sequences in the region (01] Due to the
system parameter γ1 and γ2 which have smaller value range we set γ1 = 017 and γ2 =
014 other parameters can be seen as secret keys
Algorithm
1A random key is generated in binary form ( 1times256) and it is stored in a array
2The random key generated is translated to map format using block processing (4times4)
3 The row and column wise transformation is carried out
4The key is now used to encrypt the cover image
52D logistic image undergoes substitution and permutation (column and row wise
shuffling is done)
34 DNA Sequence
A single DNA sequence is made up of four nucleic acid
bases A (adenine) C (cytosine) G (guanine) and T (thymine) where A and T are
complements and C and G are complements Let binary number 0 and 1 be
complements so 00 and 11 are complements and 01 and 10 are complements Thus we
can use these four bases A T G and C to encode 01 10 00 and 11 respectively The
encoding method still satisfies the Watson-Crick complement rule Usually each pixel
value of the 8 bit grey image can be expressed to 8 bits binary stream The binary stream
can be encoded to a DNA sequence whose length is 4 For example if the first pixel
19
value of the original image is 75 convert it into a binary stream [01001011] By using the
above DNA encoding rule to encode the stream we can get a DNA sequence [AGTC]
whereas we use A T G and C to express 01 10 00 and 11 respectively We can get a
binary sequence [01001011]
35 Attacks
The steganographic algorithm is used to embed secret messages into cover
image To obtain stego image while exchanging these stego-image through the public
communication channel various attacks have been made The are generally classified
into two types intentional or unintentional attacks Examples of unintentional attacks are
transmission errors lossy compression and changing the visual properties of the stego
document Intentional attacks on the other hand are deliberate attempts to distinguish
stego-objects from unmodified objects and thus detect the presence of covert
communication Attack methods generally exploit the fact that embedding information
usually changes the statistical properties of the objects compared to typical unmodified
objects In this proposed algorithm various attacks have been applied on the encrypted
image They are as follows
1)Shearing
2) Image Scaling
3) Image Rotating
4) Image color reduction
5) Image blurred
6) Image flip
7) cropping and intensity transformation
8) Image sharpening
9) Gaussian Noise and filtering
10) Image Contrast
11) Speckle Noise and Filtering
20
351 Shearing
The image is resized with the scale factor Resized image is rotated with
angle(theta)Finally spatial transformation from control point pairs is implemented
For example
Scale factor 09
Theta10
Fig 32 Shearing Image
352 Image Scaling
It resizes the image with a scale factor and rotation is performed It rotates the
image by angle (degrees) in a counterclockwise direction around its center point To
rotate the image clockwise specify a negative value for angle It makes the output image
large enough to contain the entire rotated image It uses nearest neighbour interpolation
setting the values of pixels in Output image that are outside the rotated image to 0 (zero)
For example
Scale Factor07
Theta30
scaling Image
21
Fig 33 Scaling Image
353 Rotation
It rotates the image by angle degrees in a counterclockwise direction around its
center point To rotate the image clockwise specify a negative value for angle It makes
the output image large enough to contain the entire rotated image It uses nearest
neighbour interpolation setting the values of pixels in Output image that are outside the
rotated image to 0 (zero)
For Example
Theta180
Fig 34 Rotation Image
354 Colour Reduced Image
It creates an indexed image approximation of the RGB image in the array RGB by
dithering the colors in the colormap map The colormap cannot have more than 65536
resized and rotated image
Rotated image
22
colors
For Example
Indexed image with 32 Colors
Fig 35 Colour Reduced Image
355 Blur Image
The image is blurred by using N-D filtering of multidimensional images It filters
the multidimensional array of original image with the multidimensional filter The array
of original image can be logical or a nonsparse numeric array of any class and dimension
The result image has the same size and class as of original image
Fig 36 Blur Image
Color reduced image
50 100 150 200 250 300 350 400 450 500
50
100
150
200
250
300
350
400
450
500
Blurred image
23
356 Flipped Image
It flips the image upside down Flipping is used to invert the image
Fig 37 Flipped Image
357 Cropped Image
It creates an interactive crop image tool associated with the image displayed in the
current figure called the target image The crop image tool is a movable resizable
rectangle that you can position interactively using the mouse When the crop image tool
is active the pointer changes to cross hairs when it is moved over the target image
Using the mouse image to be cropped can be specified by clicking and dragging the
mouse The crop rectangle using the mouse can be moved or resized When sizing and
positioning of the crop rectangle is finished create the cropped image by double-clicking
the left mouse button or by choosing crop image from the context menu Image cropping
returns the cropped image
Flipped image
24
Fig 38 Cropped Image
358 Intensity Transformation Adjust
It maps the intensity values in grayscale image to new values in resultant image
such that 1 of data is saturated at low and high intensity of original image This
increases the contrast of the output image
Fig 39 Intensity Transformation Image
Cropped Image
Intensity Transformation
25
359 Sharpening
Input array values outside the bounds of the array are assumed to equal the nearest
array border value The image is sharpened by using N-D filtering of multidimensional
images It filters the multidimensional array of original image with the multidimensional
filter The array of original image can be logical or a nonsparse numeric array of any
class and dimension The result image has the same size and class as of original image
Fig 310 Sharpened Image
3510 Gaussian Noise and Median Filtering
It adds Gaussian noise to the images Gaussian white noise have constant mean
and variance The noise added image is filtered using Median Filtering Median filtering
is a nonlinear operation often used in image processing to reduce salt and pepper noise
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Sharpened Image
26
Fig 311 Gaussian Noise and Median Filter Image
3511 Histogram of contrast image
It enhances the contrast of images by transforming the values in an intensity
image or the values in the colormap of an indexed image so that the histogram of the
output image approximately matches a specified histogram
Fig 312 Contrast Image
Gaussian Noise
Median Filtering
Contrast Image
27
Fig 313 Histogram of Contrast Image
3512 Speckle noise and Median Filtering
It adds multiplicative noise to the image I using the equation J = I+nI where n is
uniformly distributed random noise with mean 0 and variance v The default for v is 004
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Fig 314 Speckle Noise and Median Filter Image
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Histogram of Contrast Image
0 50 100 150 200 250
Speckle Noise
Median Filtering
28
36 Proposed Algorithm
Step1 Enter two keys a private key and a public key through which the RSA algorithm is
performed
Step2These key are used for encrypting the secret data using chaotic algorithm with
threshold of 2
Step3The secret data is converted into binary format from ASCII code
Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied
Step5The encryption is carried out with the key generated randomly in binary(1times256)
Step6 The 2D logistic substitution and permutation are carried out
Step7The resulting binary sequence is added with the encrypted text in LSB
Step8The image is converted to DNA sequence and transmitted
Step9 Various Attacks have been applied on the resultant image
Step10The inverse process is carried out to retrieve the original image and data
Step11The Performance Metrics have been calculated
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
LIST OF TABLES
PAGE NO
41 Performance Metric Calculation 33
42 Performance Metric Calculation between original and 34 recovered Barbara image
LIST OF ABBREVIATIONS
2D 2 Dimensional
AD Average Difference
BER Bit Error Rate
LMSE Laplacian Mean Square Error
LSB Least Significant Bit
MD Maximum Difference
MSE Mean Square Error
NCC Normalized Cross Correlation
PSNR Peak Signal to Noise Ratio
SC Structural Content
viii
TABLE TITLE
NO
2
vii
LIST OF FIGURES
FIGURE
NO
CAPTION
PAGE
NO
11 Symmetric-key cryptography 2
12 Public key Cryptography 2
13 Categories of Image Steganography 4
14 Reversible Data Hiding System 5
31 Work Flow Diagram 14
32 Shearing Image 20
33 Scaling Image 20
34 Rotation image 21
35 Colour Reduced Image 22
36 Blur Image 22
37 Flipped Image 23
38 Cropped Image 24
39 Intensity Transformation Image 24
310 Sharpened Image 25
311 Gaussian Noise and Median Filter Image 26
312 Contrast Image 26
313 Histogram of Contrast Image 27
314 Speckle Noise and Median Filter Image 27
41 Gray Scale Cover image of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
31
42 Input Image and 2D Logistic Encrypted Image 32
43 DNA Sequence 32
44 Recovered Image 33
44 Recovered Text 33
2
1
CHAPTER 1
INTRODUCTION
In an information sharing environment security of information plays an important
role Some information that is sensitive or confidential in nature must be kept private
With the introduction of computers the need for automated tools for protecting files and
other information stored in the computer become evident Transmission of sensitive
information via an open internet channel increases the risk of interception There are
many techniques proposed to deal with this issue They are
1) Cryptography
2) Steganography
3) Reversible Data Hiding
11 CRYPTOGRAPHY
Cryptography is the practice and study of techniques for secure communication in
the presence of third parties (called adversaries) More generally it is about constructing
and analyzing protocols that overcome the influence of adversaries This technique alters
the form of the message at the sender and transmits it At the receiver the original
message is extracted It mainly involves 2 operations
Encryption It is the process of the conversion of information from a readable state to
apparent nonsense with the usage of a key It is done by the sender
Decryption It is the reverse process of encryption That is it is the process of converting
scrambled message into the original one with the help of key The key may be similar to
the one which is used in encryption or it may be a different one It is done at the receiver
side
The cryptography is characterized by 3 independent dimensions
2
1) The type of operations used for transforming Plaintext to Cipher text
All encryption algorithms are based on two general principles They are
substitution and transposition Substitution is the one in which each element in the plain
text is transformed into another element Transposition is the one in which elements in
the plain text are rearranged The fundamental condition is that no information be lost
2) The Number of keys used
Based on this we can classify the techniques into two
a) Symmetric-key Cryptography Symmetric-key cryptography refers to encryption
methods in which both the sender and receiver share the same key (or less commonly in
which their keys are different but related in an easily computable way)
Figure 11 Symmetric-key cryptography
b) Public key Cryptography In public-key cryptosystems the public key may be freely
distributed while its paired private key must remain secret In a public-key encryption
system the public key is used for encryption while the private or secret key is used for
decryption
Figure 12 Public key Cryptography
3
3) The way in which the plaintext is processed
There are 2 types
a) Block Cipher It processes the input one block of elements at a time producing an
output block for each input block
b) Stream Cipher It processes the input elements continuously producing output one
element at a time as it goes along
12 STEGANOGRAPHY
It is the art and science of encoding hidden messages in such a way that no one
apart from the sender and intended recipient suspects the existence of the message It is a
form of security through obscurity Generally the hidden messages will appear to be (or
be part of) something else images articles shopping lists or some other cover texts
Plainly visible encrypted messages no matter how unbreakable will arouse interest and
may in themselves be incriminating in countries where encryption is illegal For example
the hidden message may be in invisible ink between the visible lines of a private letter
The advantage of steganography over cryptography alone is that the intended secret
message does not attract attention to itself as an object of scrutiny So cryptography is the
practice of protecting the contents of a message alone steganography is concerned with
concealing the fact that a secret message is being sent as well as concealing the contents
of the message Steganography includes the concealment of information within computer
files In digital steganography electronic communications may include steganographic
coding inside of a transport layer such as a document file image file program or
protocol Media files are ideal for steganographic transmission because of their large size
There has been a rapid growth of interest in steganography for two main reasons
(i) The publishing and broadcasting industries have become interested in techniques for
hiding encrypted copyright marks and serial numbers in digital films audio
recordings books and multimedia products
(ii) Moves by various governments to restrict the availability of encryption services
have motivated people to study methods by which private messages can be
4
embedded in seemingly innocuous cover messages
Fig 13 Categories of Image Steganography
There are many applications for digital steganography of image including
copyright protection feature tagging and secret communication Copyright notice or
watermark can embedded inside an image to identify it as intellectual property If
someone attempts to use this image without permission we can prove by extracting the
watermark In feature tagging captions annotations time stamps and other descriptive
elements can be embedded inside an image Copying the stegondashimage also copies of the
embedded features and only parties who posses the decoding stego-key will be able to
extract and view the features On the other hand secret communication does not advertise
a covert communication by using steganography Therefore it can avoid scrutiny of the
sender message and recipient This is effective only if the hidden communication is not
detected by the others people In general steganography is two types reversible and
irreversible
5
13 Reversible Data Hiding
Figure 14 Reversible Data Hiding System
Secret Message The secret message or information to hide
Cover File Digital Medium The data or medium which concealed the secret message
Stego File A modified version of cover that contains the secret message
Key Additional secret data that is needed for the embedding and extracting processes
and must be known to both the sender and the recipient
Steganographic Method A steganographic function that takes cover secret message
and key as parameters and produces stego as output
Inverse of Steganographic Method A steganographic function that has stego and key
as parameters and produces secret message as output This is the inverse of method used
in embeding process in the sense that the result of the extracting process is identical to the
input of the embedding process
6
CHAPTER 2
LITERATURE SURVEY
1) Nidhi Sethi Deepika Sharma (2014) ldquoA Novel Method Of Image
Encryption using Logistic Mappingrdquo International Journal of Computer
Science Engineering (IJCSE)
This paper presents a new method to develop secure image-encryption techniques
using a logistics based encryption algorithm In this technique a Haar wavelet transform
was used to decompose the image and decorrelate its pixels into averaging and
differencing components The logistic based encryption algorithm produces a cipher of
the test image that has good diffusion and confusion properties The remaining
components (the differencing components) are compressed using a wavelet transform
Many test images are used to demonstrate the validity of the proposed algorithm The
results of several experiments show that the proposed algorithm for image cryptosystems
provides an efficient and secure approach to real-time image encryption and transmission
To send the keys in secure form steganography will be used Steganographic techniques
allow one party to communicate information to another party without a third party even
knowing that the communication is occurring
Advantages
(i) Efficient approach
(ii) Secure key transmission
(iii) Better image quality
7
2) Jing Wang Guoping Jiang (2013) ldquoA Self-Adaptive Parallel Encryption
Algorithm Based on Discrete 2D-Logistic Maprdquo International Journal of
Modern Nonlinear Theory and Application
A self-adaptive parallel encryption algorithm based on discrete 2D-Logistic
map is developed according to the position scrambling and diffusion of multi-direction in
variable space of spatial chaos The binary sequences b1b2b3bn are obtained according
to the user key in which the binary sequence 0 and 1 denote distribution mode of
processors and the number of binary sequence n denotes cycle number Then the
pseudorandom 2D matrix is generated by 2D-Logistic map and adaptive segmentation is
applied in image matrix and pseudorandom matrix according to the value and the number
of binary sequence The parallel operation is used among blocks to improve efficiency
and meet real-time demand in transmission processes However the pixel permutation is
applied in partitioned matrix through ergodic matrix generated by pseudo-random matrix-
block to decrease the correlation of adjacent pixels Then the pixel substitution is used for
fully diffusing through cipher block chaining mode until n cycles The proposed
algorithm can meet the three requirements of parallel operation in image encryption and
the real-time requirement in transmission processes The security is proved by theoretical
analysis and simulation results
Advantages
1Security is provided
2Effeciency is improved
8
3) LJani Anbarasi GSAnandha Mala Modigari Narendra (2014) ldquoDNA
based Multi-Secret Image Sharingrdquo International Conference on
Information and Communication Technologies (ICICT)
Multiple secret sharing algorithm using the YCH scheme combined with
DNA encoding is proposed focusing at better security Firstly DNA encoding for
multiple images is carried out then the addition of these encoded components by DNA is
performed Secondly the (t n) scheme used the Lagrange interpolation polynomial to
share these DNA scrambled matrices is performed bdquot‟ denotes threshold value and bdquon‟
denotes the number of participants The resulting scrambled images are encrypted into n
shares using Shamir‟s polynomial Thirdly these shares are embedded using a modular
operation Finally bdquot‟ or more shares are pooled which reconstructs the scrambled
matrices and by decoding the DNA scrambled matrices multiple secrets are
reconstructed without loss The simulation results and the security analysis prove that this
algorithm is perfect and produces results with better PSNR value The correlation co-
efficient shows that this also has the ability of resisting various attacks
Advantages
1Security is better
2Resistance against Attack
9
4)Della Babya Jitha Thomasa Gisny Augustinea Elsa Georgea Neenu
Rosia Michaela (2014) ldquoA Novel DWT based Image Securing Method using
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Steganography is a data hiding technique that is widely used in various
information securing applications Steganography transmits data by hiding the existence
of the message so that a viewer cannot identify the transmission of message and hence
not able to decrypt it This work proposes a data securing technique that is used for
hiding multiple color images into a single color image using the Discrete Wavelet
Transform The cover image is split up into R G and B planes Secret images are
embedded into these planes An N-level decomposition of the cover image and the secret
images are done and some frequency components of the same are combined Secret
images are then extracted from the stego image Here the stego image obtained has a less
perceptible changes compared to the original image with high overall security
Advantages
1Less perceptible changes
2Overall security is high
10
5)Prasenjit Dasa Subhrajyoti Deba Nirmalya Kara Baby
Bhattacharyaa(2014)ldquoAn Improved DNA based Dual Cover
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Dual cover steganography is an evolving technique in the field of covert
data transmission This paper focuses on the concept of using a theoretical single stranded
DNA (ssDNA) as a primary cover which is extracted from an inconspicuous cover
image They have analyzed the security loopholes and performance issues of the existing
algorithm and proposed an improved algorithm on the same basisthey use 2D Logistic
map for encrypting the cover imageThen overall encryption is RC43 types of encryption
is generally used Performance of both the algorithms are tested against several visual
and statistical attacks and parameterized in terms of both security and capacity The
comparison shows that the proposed improvements provide better overall security
Advantages
1 Robustness against various attack
2 Performance measure are calculated
3 Data hiding improves security
11
6) Bhavana Agrawal Himani Agrawal ( May-2013) ldquoImplementation of AES
and RSA Using Chaos systemrdquo International Journal of Scientific amp
Engineering Research Vol 4 No 5
This paper presents two cryptographic algorithm AES and RSA Using Chaos
Chaos has attracted much attention in the field of cryptography It describes a system
which is sensitive to initial condition It generates apparently random behavior but at the
same time is completely deterministic Chaos function is used to increase the complexity
and Security of the SystemAES and RSA are the two cryptographic algorithms In AES
we apply the Chaos on S-box where as in RSA we mix the plaintext with Chaos sequence
First then apply for encryption and decryption After Implementing AES and RSA they
compare both the technique on the basis of speed
Advantages
1Chaos function is used to improve complexity
2The speed has been improved with combined technique of AES and RSA along with
chaos technique
12
7) Pahrul Irfan Yudi Prayudi Imam Riadi ( August 2015) ldquo Image
Encryption using Combination of Chaotic System and Rivers Shamir
Adleman (RSA)rdquo International Journal of Computer Applications Vol 123
No6
Security and confidentiality of data or information at the present time has
become an important concern Advanced methods for secure transmission storage and
retrieval of digital images are increasingly needed for a number of military medical
homeland security and other applications Various kinds of techniques for increase
security data or information already is developed one common way is by cryptographic
techniques Cryptography is science to maintain the security of the message by changing
data or information into a different form so the message cannot be recognized To
compensate for increasing computing speeds increases it takes more than one encryption
algorithm to improve security of digital images One way is by using algorithms to
double cryptography do encryption and decryption Cryptographic algorithm often used
today and the proven strength specially the digital image is Algorithm with Chaos
system To improve security at the image then we use Additional algorithms namely
Rivers algorithm Shamir Adleman (RSA) which known as the standard of cryptography
algorithms This research aims to optimize security bitmap image format by combining
the two algorithms namely Chaos-based algorithms and RSA algorithm into one
application Experiments conducted show that the proposed algorithm possesses robust
security features such as fairly uniform distribution high sensitivity to both keys and
plain images almost ideal entropy and the ability to highly de-correlate adjacent pixels
in the cipher images Furthermore it has a large key space and transform image to pure
text file which greatly increases its security for image encryption
Advantages
1 It aims to optimize security bitmap image format by combining the two algorithms
namely Chaos-based algorithms and RSA algorithm into one application
13
8) Weiming Zhang KedeMa NenghaiYu(2013) ldquo Reversibility improved
data hiding in encrypted imagesrdquo School of Information Science and
Technology
A novel reversible data hiding technique in encrypted images is presented in this
paper Instead of embedding data in encrypted images directly some pixels are estimated
before encryption so that additional data can be embedded in the estimating errors A
bench mark encryption algorithm (eg AES) is applied to the rest pixels of the image and
a special encryption scheme is designed to encrypt the estimating errors Without the
encryption key one cannot get access to the original image However provided with the
data hiding key only he can embed in or extract from the encrypted image additional data
without knowledge about the original image Moreover the data extraction and image
recovery are free of errors for all images Experiments demonstrate the feasibility and
efficiency of the proposed method especially in aspect of embedding rate versus Peak
Signal-to-Noise Ratio (PSNR)
The paper proposes a novel method to significantly improve the performance by
reversing the order of encryption and vacating room In the light of this idea we empty
out room prior to image encryption by shifting the histogram of estimating errors of some
pixels and the emptied out room will be used for data hiding The proposed method is
composed of four primary steps vacating room and encrypting image data hiding in the
encrypted image data extraction and image recovery Two different schemes extraction
before decryption and decryption before extraction are raised to cope with different
applications
Advantages
(i) Achieves excellent performance in three aspects complete reversibility PSNR
under given embedding rate separability between data higher extraction and
image decryption
14
CHAPTER 3
PROPOSED METHODOLOGY
The proposed data hiding scheme aims at the security of the hidden data
Embedding is performed in spatial domain The data to be embedded is converted into
binary form from ASCII code using chaos encryption and is embedded into the cover
image obtained after 2D logistic map This embedded image is secured using symmetric
key (K1)They are converted into DNA sequence to provide additional level of security
The hidden data can be extracted from the cover image accurately with the help of
decryption techniques and secret key (K1) The cover image can also be extracted
without any distortion The fig 31 shows the workflow
Fig 31 Work Flow Diagram
SECRET DATA
COVER IMAGE
CHAOTIC
ENCRYPTION
ENCRY 2D LOGISTIC
ENCRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
CHAOTIC
DECRYPTION
ENCRY
SECRET DATA
COVER IMAGE 2D LOGISTIC
DECRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
15
31 Chaotic Encryption
Chaotic cryptography is the application of the mathematical chaos theory to the
practice of the cryptography the study or techniques used to privately and securely
transmit information with the presence of an third-party or adversary The use of chaos
or randomness in cryptography has long been sought after by entities wanting a new way
to encrypt messages However because of the lack of thorough provable security
properties and low acceptable performance chaotic cryptography has encountered
setbacksIn order to use chaos theory acceptably in cryptography they must first be
mapped to each other Properties in chaotic systems and cryptographic primitives share
unique characteristics that allow for the chaotic systems to be applied to cryptography If
chaotic parameters as well as cryptographic keys can be mapped symmetrically or
mapped to produce acceptable and functional outputs it will make it next to impossible
for an adversary to find the outputs without any knowledge the initial values Since
chaotic maps in a real life scenario require a set of numbers that are limited they may in
fact have no real purpose in a cryptosystem if the chaotic behavior can be predicted To
counter this possibility there exists simple to advanced ciphers Chaos theory used in
cryptosystems for commercial implementation has proven to be unsuccessful mainly
because a chaos theories‟ requirement to use intervals of real numbers Given enough
resources and time an adversary could be able to predict functional outcomes Since
chaotic cryptosystems have no root in number theory this would make it difficult or
impossible to implement therefore impractical
32 The RSA Algorithm
The RSA cryptosystem named after its inventors R Rivest A Shamir and L
Adleman is the most widely used public key Cryptosystem It may be used to provide
both secrecy and digital signatures and its security is based on the intractability of the
integer factorizationThe RSA algorithm involves three steps key generation encryption
and decryption
16
321 Key Generation
RSA involves a public key and a private key The public key can be known to
everyone and is used for encrypting messages Messages encrypted with the public key
can only be decrypted in a reasonable amount of time using the private key The keys for
the RSA algorithm are generated the following way To generate the two keys choose
two random large prime numbers p and q For maximum security choose p and q of
equal length Then randomly choose the encryption key e such that e and ( p minus1) (q minus1)
are relatively prime Finally use the extended Euclidean algorithm to compute the
decryption key d such that
d= e-1
mod ( (p-1) (q-1))
Note that d and n are also relatively prime The numbers e and K are the public
key the number d is the private key The two primes p and q are no longer needed They
should be discarded but never revealed
322 Encryption
Firstly receiver transmits her public key (n e) to sender and keeps the private key
secret If sender wishes to send message M to receiver Sender change the message M in
to integer m such that 0 le mltn Then sender computes the cipher text c corresponding to
Cequiv me
(mod n)
323 Decryption
Receiver can recover M from c by using private key exponent d via computing
M equiv cd
(mod n)
17
Algorithm
1Select any two prime numbers say (pq)
2Compute n=pq and also compute empty(119899)=(p-1)(q-1)
3Choose e such that 1ltelt empty(119899)
4Choose d such that (de)mod empty(119899)=1
5Public key is (en) and Private key is (dn)
6 If egt=2 then check i==1 if so return 1 else return 0
7In a iteration check for e(i)==1 if so take mod function of message with n
8Message is converted to cipher text in ASCII form with the key generated
9The cipher data in ASCII form is converted to binary form
33 2D Logistic Encryption
The chaotic system is a deterministic nonlinear system It possesses a varied
characteristics such as high sensitivity to initial conditions and system parameters
random-like behaviors and so forth Chaotic sequences produced by chaotic maps are
pseudo-random sequences their structures are very complex and difficult to be analyzed
and predicted In other words chaotic systems can improve the security of encryption
systems Thus it is advisable to encrypt digital image with chaotic systems There are
two chaotic maps one is logistic map and the other is 2D logistic map In the proposed
work 2D logistic map is used
Logistic map is an example for chaotic map and it is described as follows
x(n+1)=μ x(n)(1minusx(n))
μ is a positive constant sometimes known as the biotic potential gives the so-called
logistic map x(n) is series of values used to plot Logistic mapwhere μ isin [04] x(n) isin
(01) and n = 01 2hellip The research result shows that the system is in chaotic state
under the condition that 356994 lt μ le 4
18
2D logistic map is described in as follows
119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2
119910119894+1 = 1205832 lowast 119910119894(1 minus 119910119894) + 1205732((119909119894)2 + 119909119894 lowast 119910119894)
Where z(xy) is the logistic map 1205831 and 1205832 are positive constant sometimes known as the
biotic potential gives the so-called logistic map x be the position of images in x axis
and y be the position of images in y axis 1205731 and 1205732 are the correlation constantsWhen
275 lt μ1 le 34 275 lt μ2 le 345 015 lt 1205731le 021 and 013 lt 1205732 le 015 the system is in
chaotic state and can generate two chaotic sequences in the region (01] Due to the
system parameter γ1 and γ2 which have smaller value range we set γ1 = 017 and γ2 =
014 other parameters can be seen as secret keys
Algorithm
1A random key is generated in binary form ( 1times256) and it is stored in a array
2The random key generated is translated to map format using block processing (4times4)
3 The row and column wise transformation is carried out
4The key is now used to encrypt the cover image
52D logistic image undergoes substitution and permutation (column and row wise
shuffling is done)
34 DNA Sequence
A single DNA sequence is made up of four nucleic acid
bases A (adenine) C (cytosine) G (guanine) and T (thymine) where A and T are
complements and C and G are complements Let binary number 0 and 1 be
complements so 00 and 11 are complements and 01 and 10 are complements Thus we
can use these four bases A T G and C to encode 01 10 00 and 11 respectively The
encoding method still satisfies the Watson-Crick complement rule Usually each pixel
value of the 8 bit grey image can be expressed to 8 bits binary stream The binary stream
can be encoded to a DNA sequence whose length is 4 For example if the first pixel
19
value of the original image is 75 convert it into a binary stream [01001011] By using the
above DNA encoding rule to encode the stream we can get a DNA sequence [AGTC]
whereas we use A T G and C to express 01 10 00 and 11 respectively We can get a
binary sequence [01001011]
35 Attacks
The steganographic algorithm is used to embed secret messages into cover
image To obtain stego image while exchanging these stego-image through the public
communication channel various attacks have been made The are generally classified
into two types intentional or unintentional attacks Examples of unintentional attacks are
transmission errors lossy compression and changing the visual properties of the stego
document Intentional attacks on the other hand are deliberate attempts to distinguish
stego-objects from unmodified objects and thus detect the presence of covert
communication Attack methods generally exploit the fact that embedding information
usually changes the statistical properties of the objects compared to typical unmodified
objects In this proposed algorithm various attacks have been applied on the encrypted
image They are as follows
1)Shearing
2) Image Scaling
3) Image Rotating
4) Image color reduction
5) Image blurred
6) Image flip
7) cropping and intensity transformation
8) Image sharpening
9) Gaussian Noise and filtering
10) Image Contrast
11) Speckle Noise and Filtering
20
351 Shearing
The image is resized with the scale factor Resized image is rotated with
angle(theta)Finally spatial transformation from control point pairs is implemented
For example
Scale factor 09
Theta10
Fig 32 Shearing Image
352 Image Scaling
It resizes the image with a scale factor and rotation is performed It rotates the
image by angle (degrees) in a counterclockwise direction around its center point To
rotate the image clockwise specify a negative value for angle It makes the output image
large enough to contain the entire rotated image It uses nearest neighbour interpolation
setting the values of pixels in Output image that are outside the rotated image to 0 (zero)
For example
Scale Factor07
Theta30
scaling Image
21
Fig 33 Scaling Image
353 Rotation
It rotates the image by angle degrees in a counterclockwise direction around its
center point To rotate the image clockwise specify a negative value for angle It makes
the output image large enough to contain the entire rotated image It uses nearest
neighbour interpolation setting the values of pixels in Output image that are outside the
rotated image to 0 (zero)
For Example
Theta180
Fig 34 Rotation Image
354 Colour Reduced Image
It creates an indexed image approximation of the RGB image in the array RGB by
dithering the colors in the colormap map The colormap cannot have more than 65536
resized and rotated image
Rotated image
22
colors
For Example
Indexed image with 32 Colors
Fig 35 Colour Reduced Image
355 Blur Image
The image is blurred by using N-D filtering of multidimensional images It filters
the multidimensional array of original image with the multidimensional filter The array
of original image can be logical or a nonsparse numeric array of any class and dimension
The result image has the same size and class as of original image
Fig 36 Blur Image
Color reduced image
50 100 150 200 250 300 350 400 450 500
50
100
150
200
250
300
350
400
450
500
Blurred image
23
356 Flipped Image
It flips the image upside down Flipping is used to invert the image
Fig 37 Flipped Image
357 Cropped Image
It creates an interactive crop image tool associated with the image displayed in the
current figure called the target image The crop image tool is a movable resizable
rectangle that you can position interactively using the mouse When the crop image tool
is active the pointer changes to cross hairs when it is moved over the target image
Using the mouse image to be cropped can be specified by clicking and dragging the
mouse The crop rectangle using the mouse can be moved or resized When sizing and
positioning of the crop rectangle is finished create the cropped image by double-clicking
the left mouse button or by choosing crop image from the context menu Image cropping
returns the cropped image
Flipped image
24
Fig 38 Cropped Image
358 Intensity Transformation Adjust
It maps the intensity values in grayscale image to new values in resultant image
such that 1 of data is saturated at low and high intensity of original image This
increases the contrast of the output image
Fig 39 Intensity Transformation Image
Cropped Image
Intensity Transformation
25
359 Sharpening
Input array values outside the bounds of the array are assumed to equal the nearest
array border value The image is sharpened by using N-D filtering of multidimensional
images It filters the multidimensional array of original image with the multidimensional
filter The array of original image can be logical or a nonsparse numeric array of any
class and dimension The result image has the same size and class as of original image
Fig 310 Sharpened Image
3510 Gaussian Noise and Median Filtering
It adds Gaussian noise to the images Gaussian white noise have constant mean
and variance The noise added image is filtered using Median Filtering Median filtering
is a nonlinear operation often used in image processing to reduce salt and pepper noise
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Sharpened Image
26
Fig 311 Gaussian Noise and Median Filter Image
3511 Histogram of contrast image
It enhances the contrast of images by transforming the values in an intensity
image or the values in the colormap of an indexed image so that the histogram of the
output image approximately matches a specified histogram
Fig 312 Contrast Image
Gaussian Noise
Median Filtering
Contrast Image
27
Fig 313 Histogram of Contrast Image
3512 Speckle noise and Median Filtering
It adds multiplicative noise to the image I using the equation J = I+nI where n is
uniformly distributed random noise with mean 0 and variance v The default for v is 004
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Fig 314 Speckle Noise and Median Filter Image
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Histogram of Contrast Image
0 50 100 150 200 250
Speckle Noise
Median Filtering
28
36 Proposed Algorithm
Step1 Enter two keys a private key and a public key through which the RSA algorithm is
performed
Step2These key are used for encrypting the secret data using chaotic algorithm with
threshold of 2
Step3The secret data is converted into binary format from ASCII code
Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied
Step5The encryption is carried out with the key generated randomly in binary(1times256)
Step6 The 2D logistic substitution and permutation are carried out
Step7The resulting binary sequence is added with the encrypted text in LSB
Step8The image is converted to DNA sequence and transmitted
Step9 Various Attacks have been applied on the resultant image
Step10The inverse process is carried out to retrieve the original image and data
Step11The Performance Metrics have been calculated
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
2
vii
LIST OF FIGURES
FIGURE
NO
CAPTION
PAGE
NO
11 Symmetric-key cryptography 2
12 Public key Cryptography 2
13 Categories of Image Steganography 4
14 Reversible Data Hiding System 5
31 Work Flow Diagram 14
32 Shearing Image 20
33 Scaling Image 20
34 Rotation image 21
35 Colour Reduced Image 22
36 Blur Image 22
37 Flipped Image 23
38 Cropped Image 24
39 Intensity Transformation Image 24
310 Sharpened Image 25
311 Gaussian Noise and Median Filter Image 26
312 Contrast Image 26
313 Histogram of Contrast Image 27
314 Speckle Noise and Median Filter Image 27
41 Gray Scale Cover image of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
31
42 Input Image and 2D Logistic Encrypted Image 32
43 DNA Sequence 32
44 Recovered Image 33
44 Recovered Text 33
2
1
CHAPTER 1
INTRODUCTION
In an information sharing environment security of information plays an important
role Some information that is sensitive or confidential in nature must be kept private
With the introduction of computers the need for automated tools for protecting files and
other information stored in the computer become evident Transmission of sensitive
information via an open internet channel increases the risk of interception There are
many techniques proposed to deal with this issue They are
1) Cryptography
2) Steganography
3) Reversible Data Hiding
11 CRYPTOGRAPHY
Cryptography is the practice and study of techniques for secure communication in
the presence of third parties (called adversaries) More generally it is about constructing
and analyzing protocols that overcome the influence of adversaries This technique alters
the form of the message at the sender and transmits it At the receiver the original
message is extracted It mainly involves 2 operations
Encryption It is the process of the conversion of information from a readable state to
apparent nonsense with the usage of a key It is done by the sender
Decryption It is the reverse process of encryption That is it is the process of converting
scrambled message into the original one with the help of key The key may be similar to
the one which is used in encryption or it may be a different one It is done at the receiver
side
The cryptography is characterized by 3 independent dimensions
2
1) The type of operations used for transforming Plaintext to Cipher text
All encryption algorithms are based on two general principles They are
substitution and transposition Substitution is the one in which each element in the plain
text is transformed into another element Transposition is the one in which elements in
the plain text are rearranged The fundamental condition is that no information be lost
2) The Number of keys used
Based on this we can classify the techniques into two
a) Symmetric-key Cryptography Symmetric-key cryptography refers to encryption
methods in which both the sender and receiver share the same key (or less commonly in
which their keys are different but related in an easily computable way)
Figure 11 Symmetric-key cryptography
b) Public key Cryptography In public-key cryptosystems the public key may be freely
distributed while its paired private key must remain secret In a public-key encryption
system the public key is used for encryption while the private or secret key is used for
decryption
Figure 12 Public key Cryptography
3
3) The way in which the plaintext is processed
There are 2 types
a) Block Cipher It processes the input one block of elements at a time producing an
output block for each input block
b) Stream Cipher It processes the input elements continuously producing output one
element at a time as it goes along
12 STEGANOGRAPHY
It is the art and science of encoding hidden messages in such a way that no one
apart from the sender and intended recipient suspects the existence of the message It is a
form of security through obscurity Generally the hidden messages will appear to be (or
be part of) something else images articles shopping lists or some other cover texts
Plainly visible encrypted messages no matter how unbreakable will arouse interest and
may in themselves be incriminating in countries where encryption is illegal For example
the hidden message may be in invisible ink between the visible lines of a private letter
The advantage of steganography over cryptography alone is that the intended secret
message does not attract attention to itself as an object of scrutiny So cryptography is the
practice of protecting the contents of a message alone steganography is concerned with
concealing the fact that a secret message is being sent as well as concealing the contents
of the message Steganography includes the concealment of information within computer
files In digital steganography electronic communications may include steganographic
coding inside of a transport layer such as a document file image file program or
protocol Media files are ideal for steganographic transmission because of their large size
There has been a rapid growth of interest in steganography for two main reasons
(i) The publishing and broadcasting industries have become interested in techniques for
hiding encrypted copyright marks and serial numbers in digital films audio
recordings books and multimedia products
(ii) Moves by various governments to restrict the availability of encryption services
have motivated people to study methods by which private messages can be
4
embedded in seemingly innocuous cover messages
Fig 13 Categories of Image Steganography
There are many applications for digital steganography of image including
copyright protection feature tagging and secret communication Copyright notice or
watermark can embedded inside an image to identify it as intellectual property If
someone attempts to use this image without permission we can prove by extracting the
watermark In feature tagging captions annotations time stamps and other descriptive
elements can be embedded inside an image Copying the stegondashimage also copies of the
embedded features and only parties who posses the decoding stego-key will be able to
extract and view the features On the other hand secret communication does not advertise
a covert communication by using steganography Therefore it can avoid scrutiny of the
sender message and recipient This is effective only if the hidden communication is not
detected by the others people In general steganography is two types reversible and
irreversible
5
13 Reversible Data Hiding
Figure 14 Reversible Data Hiding System
Secret Message The secret message or information to hide
Cover File Digital Medium The data or medium which concealed the secret message
Stego File A modified version of cover that contains the secret message
Key Additional secret data that is needed for the embedding and extracting processes
and must be known to both the sender and the recipient
Steganographic Method A steganographic function that takes cover secret message
and key as parameters and produces stego as output
Inverse of Steganographic Method A steganographic function that has stego and key
as parameters and produces secret message as output This is the inverse of method used
in embeding process in the sense that the result of the extracting process is identical to the
input of the embedding process
6
CHAPTER 2
LITERATURE SURVEY
1) Nidhi Sethi Deepika Sharma (2014) ldquoA Novel Method Of Image
Encryption using Logistic Mappingrdquo International Journal of Computer
Science Engineering (IJCSE)
This paper presents a new method to develop secure image-encryption techniques
using a logistics based encryption algorithm In this technique a Haar wavelet transform
was used to decompose the image and decorrelate its pixels into averaging and
differencing components The logistic based encryption algorithm produces a cipher of
the test image that has good diffusion and confusion properties The remaining
components (the differencing components) are compressed using a wavelet transform
Many test images are used to demonstrate the validity of the proposed algorithm The
results of several experiments show that the proposed algorithm for image cryptosystems
provides an efficient and secure approach to real-time image encryption and transmission
To send the keys in secure form steganography will be used Steganographic techniques
allow one party to communicate information to another party without a third party even
knowing that the communication is occurring
Advantages
(i) Efficient approach
(ii) Secure key transmission
(iii) Better image quality
7
2) Jing Wang Guoping Jiang (2013) ldquoA Self-Adaptive Parallel Encryption
Algorithm Based on Discrete 2D-Logistic Maprdquo International Journal of
Modern Nonlinear Theory and Application
A self-adaptive parallel encryption algorithm based on discrete 2D-Logistic
map is developed according to the position scrambling and diffusion of multi-direction in
variable space of spatial chaos The binary sequences b1b2b3bn are obtained according
to the user key in which the binary sequence 0 and 1 denote distribution mode of
processors and the number of binary sequence n denotes cycle number Then the
pseudorandom 2D matrix is generated by 2D-Logistic map and adaptive segmentation is
applied in image matrix and pseudorandom matrix according to the value and the number
of binary sequence The parallel operation is used among blocks to improve efficiency
and meet real-time demand in transmission processes However the pixel permutation is
applied in partitioned matrix through ergodic matrix generated by pseudo-random matrix-
block to decrease the correlation of adjacent pixels Then the pixel substitution is used for
fully diffusing through cipher block chaining mode until n cycles The proposed
algorithm can meet the three requirements of parallel operation in image encryption and
the real-time requirement in transmission processes The security is proved by theoretical
analysis and simulation results
Advantages
1Security is provided
2Effeciency is improved
8
3) LJani Anbarasi GSAnandha Mala Modigari Narendra (2014) ldquoDNA
based Multi-Secret Image Sharingrdquo International Conference on
Information and Communication Technologies (ICICT)
Multiple secret sharing algorithm using the YCH scheme combined with
DNA encoding is proposed focusing at better security Firstly DNA encoding for
multiple images is carried out then the addition of these encoded components by DNA is
performed Secondly the (t n) scheme used the Lagrange interpolation polynomial to
share these DNA scrambled matrices is performed bdquot‟ denotes threshold value and bdquon‟
denotes the number of participants The resulting scrambled images are encrypted into n
shares using Shamir‟s polynomial Thirdly these shares are embedded using a modular
operation Finally bdquot‟ or more shares are pooled which reconstructs the scrambled
matrices and by decoding the DNA scrambled matrices multiple secrets are
reconstructed without loss The simulation results and the security analysis prove that this
algorithm is perfect and produces results with better PSNR value The correlation co-
efficient shows that this also has the ability of resisting various attacks
Advantages
1Security is better
2Resistance against Attack
9
4)Della Babya Jitha Thomasa Gisny Augustinea Elsa Georgea Neenu
Rosia Michaela (2014) ldquoA Novel DWT based Image Securing Method using
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Steganography is a data hiding technique that is widely used in various
information securing applications Steganography transmits data by hiding the existence
of the message so that a viewer cannot identify the transmission of message and hence
not able to decrypt it This work proposes a data securing technique that is used for
hiding multiple color images into a single color image using the Discrete Wavelet
Transform The cover image is split up into R G and B planes Secret images are
embedded into these planes An N-level decomposition of the cover image and the secret
images are done and some frequency components of the same are combined Secret
images are then extracted from the stego image Here the stego image obtained has a less
perceptible changes compared to the original image with high overall security
Advantages
1Less perceptible changes
2Overall security is high
10
5)Prasenjit Dasa Subhrajyoti Deba Nirmalya Kara Baby
Bhattacharyaa(2014)ldquoAn Improved DNA based Dual Cover
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Dual cover steganography is an evolving technique in the field of covert
data transmission This paper focuses on the concept of using a theoretical single stranded
DNA (ssDNA) as a primary cover which is extracted from an inconspicuous cover
image They have analyzed the security loopholes and performance issues of the existing
algorithm and proposed an improved algorithm on the same basisthey use 2D Logistic
map for encrypting the cover imageThen overall encryption is RC43 types of encryption
is generally used Performance of both the algorithms are tested against several visual
and statistical attacks and parameterized in terms of both security and capacity The
comparison shows that the proposed improvements provide better overall security
Advantages
1 Robustness against various attack
2 Performance measure are calculated
3 Data hiding improves security
11
6) Bhavana Agrawal Himani Agrawal ( May-2013) ldquoImplementation of AES
and RSA Using Chaos systemrdquo International Journal of Scientific amp
Engineering Research Vol 4 No 5
This paper presents two cryptographic algorithm AES and RSA Using Chaos
Chaos has attracted much attention in the field of cryptography It describes a system
which is sensitive to initial condition It generates apparently random behavior but at the
same time is completely deterministic Chaos function is used to increase the complexity
and Security of the SystemAES and RSA are the two cryptographic algorithms In AES
we apply the Chaos on S-box where as in RSA we mix the plaintext with Chaos sequence
First then apply for encryption and decryption After Implementing AES and RSA they
compare both the technique on the basis of speed
Advantages
1Chaos function is used to improve complexity
2The speed has been improved with combined technique of AES and RSA along with
chaos technique
12
7) Pahrul Irfan Yudi Prayudi Imam Riadi ( August 2015) ldquo Image
Encryption using Combination of Chaotic System and Rivers Shamir
Adleman (RSA)rdquo International Journal of Computer Applications Vol 123
No6
Security and confidentiality of data or information at the present time has
become an important concern Advanced methods for secure transmission storage and
retrieval of digital images are increasingly needed for a number of military medical
homeland security and other applications Various kinds of techniques for increase
security data or information already is developed one common way is by cryptographic
techniques Cryptography is science to maintain the security of the message by changing
data or information into a different form so the message cannot be recognized To
compensate for increasing computing speeds increases it takes more than one encryption
algorithm to improve security of digital images One way is by using algorithms to
double cryptography do encryption and decryption Cryptographic algorithm often used
today and the proven strength specially the digital image is Algorithm with Chaos
system To improve security at the image then we use Additional algorithms namely
Rivers algorithm Shamir Adleman (RSA) which known as the standard of cryptography
algorithms This research aims to optimize security bitmap image format by combining
the two algorithms namely Chaos-based algorithms and RSA algorithm into one
application Experiments conducted show that the proposed algorithm possesses robust
security features such as fairly uniform distribution high sensitivity to both keys and
plain images almost ideal entropy and the ability to highly de-correlate adjacent pixels
in the cipher images Furthermore it has a large key space and transform image to pure
text file which greatly increases its security for image encryption
Advantages
1 It aims to optimize security bitmap image format by combining the two algorithms
namely Chaos-based algorithms and RSA algorithm into one application
13
8) Weiming Zhang KedeMa NenghaiYu(2013) ldquo Reversibility improved
data hiding in encrypted imagesrdquo School of Information Science and
Technology
A novel reversible data hiding technique in encrypted images is presented in this
paper Instead of embedding data in encrypted images directly some pixels are estimated
before encryption so that additional data can be embedded in the estimating errors A
bench mark encryption algorithm (eg AES) is applied to the rest pixels of the image and
a special encryption scheme is designed to encrypt the estimating errors Without the
encryption key one cannot get access to the original image However provided with the
data hiding key only he can embed in or extract from the encrypted image additional data
without knowledge about the original image Moreover the data extraction and image
recovery are free of errors for all images Experiments demonstrate the feasibility and
efficiency of the proposed method especially in aspect of embedding rate versus Peak
Signal-to-Noise Ratio (PSNR)
The paper proposes a novel method to significantly improve the performance by
reversing the order of encryption and vacating room In the light of this idea we empty
out room prior to image encryption by shifting the histogram of estimating errors of some
pixels and the emptied out room will be used for data hiding The proposed method is
composed of four primary steps vacating room and encrypting image data hiding in the
encrypted image data extraction and image recovery Two different schemes extraction
before decryption and decryption before extraction are raised to cope with different
applications
Advantages
(i) Achieves excellent performance in three aspects complete reversibility PSNR
under given embedding rate separability between data higher extraction and
image decryption
14
CHAPTER 3
PROPOSED METHODOLOGY
The proposed data hiding scheme aims at the security of the hidden data
Embedding is performed in spatial domain The data to be embedded is converted into
binary form from ASCII code using chaos encryption and is embedded into the cover
image obtained after 2D logistic map This embedded image is secured using symmetric
key (K1)They are converted into DNA sequence to provide additional level of security
The hidden data can be extracted from the cover image accurately with the help of
decryption techniques and secret key (K1) The cover image can also be extracted
without any distortion The fig 31 shows the workflow
Fig 31 Work Flow Diagram
SECRET DATA
COVER IMAGE
CHAOTIC
ENCRYPTION
ENCRY 2D LOGISTIC
ENCRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
CHAOTIC
DECRYPTION
ENCRY
SECRET DATA
COVER IMAGE 2D LOGISTIC
DECRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
15
31 Chaotic Encryption
Chaotic cryptography is the application of the mathematical chaos theory to the
practice of the cryptography the study or techniques used to privately and securely
transmit information with the presence of an third-party or adversary The use of chaos
or randomness in cryptography has long been sought after by entities wanting a new way
to encrypt messages However because of the lack of thorough provable security
properties and low acceptable performance chaotic cryptography has encountered
setbacksIn order to use chaos theory acceptably in cryptography they must first be
mapped to each other Properties in chaotic systems and cryptographic primitives share
unique characteristics that allow for the chaotic systems to be applied to cryptography If
chaotic parameters as well as cryptographic keys can be mapped symmetrically or
mapped to produce acceptable and functional outputs it will make it next to impossible
for an adversary to find the outputs without any knowledge the initial values Since
chaotic maps in a real life scenario require a set of numbers that are limited they may in
fact have no real purpose in a cryptosystem if the chaotic behavior can be predicted To
counter this possibility there exists simple to advanced ciphers Chaos theory used in
cryptosystems for commercial implementation has proven to be unsuccessful mainly
because a chaos theories‟ requirement to use intervals of real numbers Given enough
resources and time an adversary could be able to predict functional outcomes Since
chaotic cryptosystems have no root in number theory this would make it difficult or
impossible to implement therefore impractical
32 The RSA Algorithm
The RSA cryptosystem named after its inventors R Rivest A Shamir and L
Adleman is the most widely used public key Cryptosystem It may be used to provide
both secrecy and digital signatures and its security is based on the intractability of the
integer factorizationThe RSA algorithm involves three steps key generation encryption
and decryption
16
321 Key Generation
RSA involves a public key and a private key The public key can be known to
everyone and is used for encrypting messages Messages encrypted with the public key
can only be decrypted in a reasonable amount of time using the private key The keys for
the RSA algorithm are generated the following way To generate the two keys choose
two random large prime numbers p and q For maximum security choose p and q of
equal length Then randomly choose the encryption key e such that e and ( p minus1) (q minus1)
are relatively prime Finally use the extended Euclidean algorithm to compute the
decryption key d such that
d= e-1
mod ( (p-1) (q-1))
Note that d and n are also relatively prime The numbers e and K are the public
key the number d is the private key The two primes p and q are no longer needed They
should be discarded but never revealed
322 Encryption
Firstly receiver transmits her public key (n e) to sender and keeps the private key
secret If sender wishes to send message M to receiver Sender change the message M in
to integer m such that 0 le mltn Then sender computes the cipher text c corresponding to
Cequiv me
(mod n)
323 Decryption
Receiver can recover M from c by using private key exponent d via computing
M equiv cd
(mod n)
17
Algorithm
1Select any two prime numbers say (pq)
2Compute n=pq and also compute empty(119899)=(p-1)(q-1)
3Choose e such that 1ltelt empty(119899)
4Choose d such that (de)mod empty(119899)=1
5Public key is (en) and Private key is (dn)
6 If egt=2 then check i==1 if so return 1 else return 0
7In a iteration check for e(i)==1 if so take mod function of message with n
8Message is converted to cipher text in ASCII form with the key generated
9The cipher data in ASCII form is converted to binary form
33 2D Logistic Encryption
The chaotic system is a deterministic nonlinear system It possesses a varied
characteristics such as high sensitivity to initial conditions and system parameters
random-like behaviors and so forth Chaotic sequences produced by chaotic maps are
pseudo-random sequences their structures are very complex and difficult to be analyzed
and predicted In other words chaotic systems can improve the security of encryption
systems Thus it is advisable to encrypt digital image with chaotic systems There are
two chaotic maps one is logistic map and the other is 2D logistic map In the proposed
work 2D logistic map is used
Logistic map is an example for chaotic map and it is described as follows
x(n+1)=μ x(n)(1minusx(n))
μ is a positive constant sometimes known as the biotic potential gives the so-called
logistic map x(n) is series of values used to plot Logistic mapwhere μ isin [04] x(n) isin
(01) and n = 01 2hellip The research result shows that the system is in chaotic state
under the condition that 356994 lt μ le 4
18
2D logistic map is described in as follows
119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2
119910119894+1 = 1205832 lowast 119910119894(1 minus 119910119894) + 1205732((119909119894)2 + 119909119894 lowast 119910119894)
Where z(xy) is the logistic map 1205831 and 1205832 are positive constant sometimes known as the
biotic potential gives the so-called logistic map x be the position of images in x axis
and y be the position of images in y axis 1205731 and 1205732 are the correlation constantsWhen
275 lt μ1 le 34 275 lt μ2 le 345 015 lt 1205731le 021 and 013 lt 1205732 le 015 the system is in
chaotic state and can generate two chaotic sequences in the region (01] Due to the
system parameter γ1 and γ2 which have smaller value range we set γ1 = 017 and γ2 =
014 other parameters can be seen as secret keys
Algorithm
1A random key is generated in binary form ( 1times256) and it is stored in a array
2The random key generated is translated to map format using block processing (4times4)
3 The row and column wise transformation is carried out
4The key is now used to encrypt the cover image
52D logistic image undergoes substitution and permutation (column and row wise
shuffling is done)
34 DNA Sequence
A single DNA sequence is made up of four nucleic acid
bases A (adenine) C (cytosine) G (guanine) and T (thymine) where A and T are
complements and C and G are complements Let binary number 0 and 1 be
complements so 00 and 11 are complements and 01 and 10 are complements Thus we
can use these four bases A T G and C to encode 01 10 00 and 11 respectively The
encoding method still satisfies the Watson-Crick complement rule Usually each pixel
value of the 8 bit grey image can be expressed to 8 bits binary stream The binary stream
can be encoded to a DNA sequence whose length is 4 For example if the first pixel
19
value of the original image is 75 convert it into a binary stream [01001011] By using the
above DNA encoding rule to encode the stream we can get a DNA sequence [AGTC]
whereas we use A T G and C to express 01 10 00 and 11 respectively We can get a
binary sequence [01001011]
35 Attacks
The steganographic algorithm is used to embed secret messages into cover
image To obtain stego image while exchanging these stego-image through the public
communication channel various attacks have been made The are generally classified
into two types intentional or unintentional attacks Examples of unintentional attacks are
transmission errors lossy compression and changing the visual properties of the stego
document Intentional attacks on the other hand are deliberate attempts to distinguish
stego-objects from unmodified objects and thus detect the presence of covert
communication Attack methods generally exploit the fact that embedding information
usually changes the statistical properties of the objects compared to typical unmodified
objects In this proposed algorithm various attacks have been applied on the encrypted
image They are as follows
1)Shearing
2) Image Scaling
3) Image Rotating
4) Image color reduction
5) Image blurred
6) Image flip
7) cropping and intensity transformation
8) Image sharpening
9) Gaussian Noise and filtering
10) Image Contrast
11) Speckle Noise and Filtering
20
351 Shearing
The image is resized with the scale factor Resized image is rotated with
angle(theta)Finally spatial transformation from control point pairs is implemented
For example
Scale factor 09
Theta10
Fig 32 Shearing Image
352 Image Scaling
It resizes the image with a scale factor and rotation is performed It rotates the
image by angle (degrees) in a counterclockwise direction around its center point To
rotate the image clockwise specify a negative value for angle It makes the output image
large enough to contain the entire rotated image It uses nearest neighbour interpolation
setting the values of pixels in Output image that are outside the rotated image to 0 (zero)
For example
Scale Factor07
Theta30
scaling Image
21
Fig 33 Scaling Image
353 Rotation
It rotates the image by angle degrees in a counterclockwise direction around its
center point To rotate the image clockwise specify a negative value for angle It makes
the output image large enough to contain the entire rotated image It uses nearest
neighbour interpolation setting the values of pixels in Output image that are outside the
rotated image to 0 (zero)
For Example
Theta180
Fig 34 Rotation Image
354 Colour Reduced Image
It creates an indexed image approximation of the RGB image in the array RGB by
dithering the colors in the colormap map The colormap cannot have more than 65536
resized and rotated image
Rotated image
22
colors
For Example
Indexed image with 32 Colors
Fig 35 Colour Reduced Image
355 Blur Image
The image is blurred by using N-D filtering of multidimensional images It filters
the multidimensional array of original image with the multidimensional filter The array
of original image can be logical or a nonsparse numeric array of any class and dimension
The result image has the same size and class as of original image
Fig 36 Blur Image
Color reduced image
50 100 150 200 250 300 350 400 450 500
50
100
150
200
250
300
350
400
450
500
Blurred image
23
356 Flipped Image
It flips the image upside down Flipping is used to invert the image
Fig 37 Flipped Image
357 Cropped Image
It creates an interactive crop image tool associated with the image displayed in the
current figure called the target image The crop image tool is a movable resizable
rectangle that you can position interactively using the mouse When the crop image tool
is active the pointer changes to cross hairs when it is moved over the target image
Using the mouse image to be cropped can be specified by clicking and dragging the
mouse The crop rectangle using the mouse can be moved or resized When sizing and
positioning of the crop rectangle is finished create the cropped image by double-clicking
the left mouse button or by choosing crop image from the context menu Image cropping
returns the cropped image
Flipped image
24
Fig 38 Cropped Image
358 Intensity Transformation Adjust
It maps the intensity values in grayscale image to new values in resultant image
such that 1 of data is saturated at low and high intensity of original image This
increases the contrast of the output image
Fig 39 Intensity Transformation Image
Cropped Image
Intensity Transformation
25
359 Sharpening
Input array values outside the bounds of the array are assumed to equal the nearest
array border value The image is sharpened by using N-D filtering of multidimensional
images It filters the multidimensional array of original image with the multidimensional
filter The array of original image can be logical or a nonsparse numeric array of any
class and dimension The result image has the same size and class as of original image
Fig 310 Sharpened Image
3510 Gaussian Noise and Median Filtering
It adds Gaussian noise to the images Gaussian white noise have constant mean
and variance The noise added image is filtered using Median Filtering Median filtering
is a nonlinear operation often used in image processing to reduce salt and pepper noise
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Sharpened Image
26
Fig 311 Gaussian Noise and Median Filter Image
3511 Histogram of contrast image
It enhances the contrast of images by transforming the values in an intensity
image or the values in the colormap of an indexed image so that the histogram of the
output image approximately matches a specified histogram
Fig 312 Contrast Image
Gaussian Noise
Median Filtering
Contrast Image
27
Fig 313 Histogram of Contrast Image
3512 Speckle noise and Median Filtering
It adds multiplicative noise to the image I using the equation J = I+nI where n is
uniformly distributed random noise with mean 0 and variance v The default for v is 004
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Fig 314 Speckle Noise and Median Filter Image
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Histogram of Contrast Image
0 50 100 150 200 250
Speckle Noise
Median Filtering
28
36 Proposed Algorithm
Step1 Enter two keys a private key and a public key through which the RSA algorithm is
performed
Step2These key are used for encrypting the secret data using chaotic algorithm with
threshold of 2
Step3The secret data is converted into binary format from ASCII code
Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied
Step5The encryption is carried out with the key generated randomly in binary(1times256)
Step6 The 2D logistic substitution and permutation are carried out
Step7The resulting binary sequence is added with the encrypted text in LSB
Step8The image is converted to DNA sequence and transmitted
Step9 Various Attacks have been applied on the resultant image
Step10The inverse process is carried out to retrieve the original image and data
Step11The Performance Metrics have been calculated
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
vii
LIST OF FIGURES
FIGURE
NO
CAPTION
PAGE
NO
11 Symmetric-key cryptography 2
12 Public key Cryptography 2
13 Categories of Image Steganography 4
14 Reversible Data Hiding System 5
31 Work Flow Diagram 14
32 Shearing Image 20
33 Scaling Image 20
34 Rotation image 21
35 Colour Reduced Image 22
36 Blur Image 22
37 Flipped Image 23
38 Cropped Image 24
39 Intensity Transformation Image 24
310 Sharpened Image 25
311 Gaussian Noise and Median Filter Image 26
312 Contrast Image 26
313 Histogram of Contrast Image 27
314 Speckle Noise and Median Filter Image 27
41 Gray Scale Cover image of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
31
42 Input Image and 2D Logistic Encrypted Image 32
43 DNA Sequence 32
44 Recovered Image 33
44 Recovered Text 33
2
1
CHAPTER 1
INTRODUCTION
In an information sharing environment security of information plays an important
role Some information that is sensitive or confidential in nature must be kept private
With the introduction of computers the need for automated tools for protecting files and
other information stored in the computer become evident Transmission of sensitive
information via an open internet channel increases the risk of interception There are
many techniques proposed to deal with this issue They are
1) Cryptography
2) Steganography
3) Reversible Data Hiding
11 CRYPTOGRAPHY
Cryptography is the practice and study of techniques for secure communication in
the presence of third parties (called adversaries) More generally it is about constructing
and analyzing protocols that overcome the influence of adversaries This technique alters
the form of the message at the sender and transmits it At the receiver the original
message is extracted It mainly involves 2 operations
Encryption It is the process of the conversion of information from a readable state to
apparent nonsense with the usage of a key It is done by the sender
Decryption It is the reverse process of encryption That is it is the process of converting
scrambled message into the original one with the help of key The key may be similar to
the one which is used in encryption or it may be a different one It is done at the receiver
side
The cryptography is characterized by 3 independent dimensions
2
1) The type of operations used for transforming Plaintext to Cipher text
All encryption algorithms are based on two general principles They are
substitution and transposition Substitution is the one in which each element in the plain
text is transformed into another element Transposition is the one in which elements in
the plain text are rearranged The fundamental condition is that no information be lost
2) The Number of keys used
Based on this we can classify the techniques into two
a) Symmetric-key Cryptography Symmetric-key cryptography refers to encryption
methods in which both the sender and receiver share the same key (or less commonly in
which their keys are different but related in an easily computable way)
Figure 11 Symmetric-key cryptography
b) Public key Cryptography In public-key cryptosystems the public key may be freely
distributed while its paired private key must remain secret In a public-key encryption
system the public key is used for encryption while the private or secret key is used for
decryption
Figure 12 Public key Cryptography
3
3) The way in which the plaintext is processed
There are 2 types
a) Block Cipher It processes the input one block of elements at a time producing an
output block for each input block
b) Stream Cipher It processes the input elements continuously producing output one
element at a time as it goes along
12 STEGANOGRAPHY
It is the art and science of encoding hidden messages in such a way that no one
apart from the sender and intended recipient suspects the existence of the message It is a
form of security through obscurity Generally the hidden messages will appear to be (or
be part of) something else images articles shopping lists or some other cover texts
Plainly visible encrypted messages no matter how unbreakable will arouse interest and
may in themselves be incriminating in countries where encryption is illegal For example
the hidden message may be in invisible ink between the visible lines of a private letter
The advantage of steganography over cryptography alone is that the intended secret
message does not attract attention to itself as an object of scrutiny So cryptography is the
practice of protecting the contents of a message alone steganography is concerned with
concealing the fact that a secret message is being sent as well as concealing the contents
of the message Steganography includes the concealment of information within computer
files In digital steganography electronic communications may include steganographic
coding inside of a transport layer such as a document file image file program or
protocol Media files are ideal for steganographic transmission because of their large size
There has been a rapid growth of interest in steganography for two main reasons
(i) The publishing and broadcasting industries have become interested in techniques for
hiding encrypted copyright marks and serial numbers in digital films audio
recordings books and multimedia products
(ii) Moves by various governments to restrict the availability of encryption services
have motivated people to study methods by which private messages can be
4
embedded in seemingly innocuous cover messages
Fig 13 Categories of Image Steganography
There are many applications for digital steganography of image including
copyright protection feature tagging and secret communication Copyright notice or
watermark can embedded inside an image to identify it as intellectual property If
someone attempts to use this image without permission we can prove by extracting the
watermark In feature tagging captions annotations time stamps and other descriptive
elements can be embedded inside an image Copying the stegondashimage also copies of the
embedded features and only parties who posses the decoding stego-key will be able to
extract and view the features On the other hand secret communication does not advertise
a covert communication by using steganography Therefore it can avoid scrutiny of the
sender message and recipient This is effective only if the hidden communication is not
detected by the others people In general steganography is two types reversible and
irreversible
5
13 Reversible Data Hiding
Figure 14 Reversible Data Hiding System
Secret Message The secret message or information to hide
Cover File Digital Medium The data or medium which concealed the secret message
Stego File A modified version of cover that contains the secret message
Key Additional secret data that is needed for the embedding and extracting processes
and must be known to both the sender and the recipient
Steganographic Method A steganographic function that takes cover secret message
and key as parameters and produces stego as output
Inverse of Steganographic Method A steganographic function that has stego and key
as parameters and produces secret message as output This is the inverse of method used
in embeding process in the sense that the result of the extracting process is identical to the
input of the embedding process
6
CHAPTER 2
LITERATURE SURVEY
1) Nidhi Sethi Deepika Sharma (2014) ldquoA Novel Method Of Image
Encryption using Logistic Mappingrdquo International Journal of Computer
Science Engineering (IJCSE)
This paper presents a new method to develop secure image-encryption techniques
using a logistics based encryption algorithm In this technique a Haar wavelet transform
was used to decompose the image and decorrelate its pixels into averaging and
differencing components The logistic based encryption algorithm produces a cipher of
the test image that has good diffusion and confusion properties The remaining
components (the differencing components) are compressed using a wavelet transform
Many test images are used to demonstrate the validity of the proposed algorithm The
results of several experiments show that the proposed algorithm for image cryptosystems
provides an efficient and secure approach to real-time image encryption and transmission
To send the keys in secure form steganography will be used Steganographic techniques
allow one party to communicate information to another party without a third party even
knowing that the communication is occurring
Advantages
(i) Efficient approach
(ii) Secure key transmission
(iii) Better image quality
7
2) Jing Wang Guoping Jiang (2013) ldquoA Self-Adaptive Parallel Encryption
Algorithm Based on Discrete 2D-Logistic Maprdquo International Journal of
Modern Nonlinear Theory and Application
A self-adaptive parallel encryption algorithm based on discrete 2D-Logistic
map is developed according to the position scrambling and diffusion of multi-direction in
variable space of spatial chaos The binary sequences b1b2b3bn are obtained according
to the user key in which the binary sequence 0 and 1 denote distribution mode of
processors and the number of binary sequence n denotes cycle number Then the
pseudorandom 2D matrix is generated by 2D-Logistic map and adaptive segmentation is
applied in image matrix and pseudorandom matrix according to the value and the number
of binary sequence The parallel operation is used among blocks to improve efficiency
and meet real-time demand in transmission processes However the pixel permutation is
applied in partitioned matrix through ergodic matrix generated by pseudo-random matrix-
block to decrease the correlation of adjacent pixels Then the pixel substitution is used for
fully diffusing through cipher block chaining mode until n cycles The proposed
algorithm can meet the three requirements of parallel operation in image encryption and
the real-time requirement in transmission processes The security is proved by theoretical
analysis and simulation results
Advantages
1Security is provided
2Effeciency is improved
8
3) LJani Anbarasi GSAnandha Mala Modigari Narendra (2014) ldquoDNA
based Multi-Secret Image Sharingrdquo International Conference on
Information and Communication Technologies (ICICT)
Multiple secret sharing algorithm using the YCH scheme combined with
DNA encoding is proposed focusing at better security Firstly DNA encoding for
multiple images is carried out then the addition of these encoded components by DNA is
performed Secondly the (t n) scheme used the Lagrange interpolation polynomial to
share these DNA scrambled matrices is performed bdquot‟ denotes threshold value and bdquon‟
denotes the number of participants The resulting scrambled images are encrypted into n
shares using Shamir‟s polynomial Thirdly these shares are embedded using a modular
operation Finally bdquot‟ or more shares are pooled which reconstructs the scrambled
matrices and by decoding the DNA scrambled matrices multiple secrets are
reconstructed without loss The simulation results and the security analysis prove that this
algorithm is perfect and produces results with better PSNR value The correlation co-
efficient shows that this also has the ability of resisting various attacks
Advantages
1Security is better
2Resistance against Attack
9
4)Della Babya Jitha Thomasa Gisny Augustinea Elsa Georgea Neenu
Rosia Michaela (2014) ldquoA Novel DWT based Image Securing Method using
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Steganography is a data hiding technique that is widely used in various
information securing applications Steganography transmits data by hiding the existence
of the message so that a viewer cannot identify the transmission of message and hence
not able to decrypt it This work proposes a data securing technique that is used for
hiding multiple color images into a single color image using the Discrete Wavelet
Transform The cover image is split up into R G and B planes Secret images are
embedded into these planes An N-level decomposition of the cover image and the secret
images are done and some frequency components of the same are combined Secret
images are then extracted from the stego image Here the stego image obtained has a less
perceptible changes compared to the original image with high overall security
Advantages
1Less perceptible changes
2Overall security is high
10
5)Prasenjit Dasa Subhrajyoti Deba Nirmalya Kara Baby
Bhattacharyaa(2014)ldquoAn Improved DNA based Dual Cover
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Dual cover steganography is an evolving technique in the field of covert
data transmission This paper focuses on the concept of using a theoretical single stranded
DNA (ssDNA) as a primary cover which is extracted from an inconspicuous cover
image They have analyzed the security loopholes and performance issues of the existing
algorithm and proposed an improved algorithm on the same basisthey use 2D Logistic
map for encrypting the cover imageThen overall encryption is RC43 types of encryption
is generally used Performance of both the algorithms are tested against several visual
and statistical attacks and parameterized in terms of both security and capacity The
comparison shows that the proposed improvements provide better overall security
Advantages
1 Robustness against various attack
2 Performance measure are calculated
3 Data hiding improves security
11
6) Bhavana Agrawal Himani Agrawal ( May-2013) ldquoImplementation of AES
and RSA Using Chaos systemrdquo International Journal of Scientific amp
Engineering Research Vol 4 No 5
This paper presents two cryptographic algorithm AES and RSA Using Chaos
Chaos has attracted much attention in the field of cryptography It describes a system
which is sensitive to initial condition It generates apparently random behavior but at the
same time is completely deterministic Chaos function is used to increase the complexity
and Security of the SystemAES and RSA are the two cryptographic algorithms In AES
we apply the Chaos on S-box where as in RSA we mix the plaintext with Chaos sequence
First then apply for encryption and decryption After Implementing AES and RSA they
compare both the technique on the basis of speed
Advantages
1Chaos function is used to improve complexity
2The speed has been improved with combined technique of AES and RSA along with
chaos technique
12
7) Pahrul Irfan Yudi Prayudi Imam Riadi ( August 2015) ldquo Image
Encryption using Combination of Chaotic System and Rivers Shamir
Adleman (RSA)rdquo International Journal of Computer Applications Vol 123
No6
Security and confidentiality of data or information at the present time has
become an important concern Advanced methods for secure transmission storage and
retrieval of digital images are increasingly needed for a number of military medical
homeland security and other applications Various kinds of techniques for increase
security data or information already is developed one common way is by cryptographic
techniques Cryptography is science to maintain the security of the message by changing
data or information into a different form so the message cannot be recognized To
compensate for increasing computing speeds increases it takes more than one encryption
algorithm to improve security of digital images One way is by using algorithms to
double cryptography do encryption and decryption Cryptographic algorithm often used
today and the proven strength specially the digital image is Algorithm with Chaos
system To improve security at the image then we use Additional algorithms namely
Rivers algorithm Shamir Adleman (RSA) which known as the standard of cryptography
algorithms This research aims to optimize security bitmap image format by combining
the two algorithms namely Chaos-based algorithms and RSA algorithm into one
application Experiments conducted show that the proposed algorithm possesses robust
security features such as fairly uniform distribution high sensitivity to both keys and
plain images almost ideal entropy and the ability to highly de-correlate adjacent pixels
in the cipher images Furthermore it has a large key space and transform image to pure
text file which greatly increases its security for image encryption
Advantages
1 It aims to optimize security bitmap image format by combining the two algorithms
namely Chaos-based algorithms and RSA algorithm into one application
13
8) Weiming Zhang KedeMa NenghaiYu(2013) ldquo Reversibility improved
data hiding in encrypted imagesrdquo School of Information Science and
Technology
A novel reversible data hiding technique in encrypted images is presented in this
paper Instead of embedding data in encrypted images directly some pixels are estimated
before encryption so that additional data can be embedded in the estimating errors A
bench mark encryption algorithm (eg AES) is applied to the rest pixels of the image and
a special encryption scheme is designed to encrypt the estimating errors Without the
encryption key one cannot get access to the original image However provided with the
data hiding key only he can embed in or extract from the encrypted image additional data
without knowledge about the original image Moreover the data extraction and image
recovery are free of errors for all images Experiments demonstrate the feasibility and
efficiency of the proposed method especially in aspect of embedding rate versus Peak
Signal-to-Noise Ratio (PSNR)
The paper proposes a novel method to significantly improve the performance by
reversing the order of encryption and vacating room In the light of this idea we empty
out room prior to image encryption by shifting the histogram of estimating errors of some
pixels and the emptied out room will be used for data hiding The proposed method is
composed of four primary steps vacating room and encrypting image data hiding in the
encrypted image data extraction and image recovery Two different schemes extraction
before decryption and decryption before extraction are raised to cope with different
applications
Advantages
(i) Achieves excellent performance in three aspects complete reversibility PSNR
under given embedding rate separability between data higher extraction and
image decryption
14
CHAPTER 3
PROPOSED METHODOLOGY
The proposed data hiding scheme aims at the security of the hidden data
Embedding is performed in spatial domain The data to be embedded is converted into
binary form from ASCII code using chaos encryption and is embedded into the cover
image obtained after 2D logistic map This embedded image is secured using symmetric
key (K1)They are converted into DNA sequence to provide additional level of security
The hidden data can be extracted from the cover image accurately with the help of
decryption techniques and secret key (K1) The cover image can also be extracted
without any distortion The fig 31 shows the workflow
Fig 31 Work Flow Diagram
SECRET DATA
COVER IMAGE
CHAOTIC
ENCRYPTION
ENCRY 2D LOGISTIC
ENCRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
CHAOTIC
DECRYPTION
ENCRY
SECRET DATA
COVER IMAGE 2D LOGISTIC
DECRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
15
31 Chaotic Encryption
Chaotic cryptography is the application of the mathematical chaos theory to the
practice of the cryptography the study or techniques used to privately and securely
transmit information with the presence of an third-party or adversary The use of chaos
or randomness in cryptography has long been sought after by entities wanting a new way
to encrypt messages However because of the lack of thorough provable security
properties and low acceptable performance chaotic cryptography has encountered
setbacksIn order to use chaos theory acceptably in cryptography they must first be
mapped to each other Properties in chaotic systems and cryptographic primitives share
unique characteristics that allow for the chaotic systems to be applied to cryptography If
chaotic parameters as well as cryptographic keys can be mapped symmetrically or
mapped to produce acceptable and functional outputs it will make it next to impossible
for an adversary to find the outputs without any knowledge the initial values Since
chaotic maps in a real life scenario require a set of numbers that are limited they may in
fact have no real purpose in a cryptosystem if the chaotic behavior can be predicted To
counter this possibility there exists simple to advanced ciphers Chaos theory used in
cryptosystems for commercial implementation has proven to be unsuccessful mainly
because a chaos theories‟ requirement to use intervals of real numbers Given enough
resources and time an adversary could be able to predict functional outcomes Since
chaotic cryptosystems have no root in number theory this would make it difficult or
impossible to implement therefore impractical
32 The RSA Algorithm
The RSA cryptosystem named after its inventors R Rivest A Shamir and L
Adleman is the most widely used public key Cryptosystem It may be used to provide
both secrecy and digital signatures and its security is based on the intractability of the
integer factorizationThe RSA algorithm involves three steps key generation encryption
and decryption
16
321 Key Generation
RSA involves a public key and a private key The public key can be known to
everyone and is used for encrypting messages Messages encrypted with the public key
can only be decrypted in a reasonable amount of time using the private key The keys for
the RSA algorithm are generated the following way To generate the two keys choose
two random large prime numbers p and q For maximum security choose p and q of
equal length Then randomly choose the encryption key e such that e and ( p minus1) (q minus1)
are relatively prime Finally use the extended Euclidean algorithm to compute the
decryption key d such that
d= e-1
mod ( (p-1) (q-1))
Note that d and n are also relatively prime The numbers e and K are the public
key the number d is the private key The two primes p and q are no longer needed They
should be discarded but never revealed
322 Encryption
Firstly receiver transmits her public key (n e) to sender and keeps the private key
secret If sender wishes to send message M to receiver Sender change the message M in
to integer m such that 0 le mltn Then sender computes the cipher text c corresponding to
Cequiv me
(mod n)
323 Decryption
Receiver can recover M from c by using private key exponent d via computing
M equiv cd
(mod n)
17
Algorithm
1Select any two prime numbers say (pq)
2Compute n=pq and also compute empty(119899)=(p-1)(q-1)
3Choose e such that 1ltelt empty(119899)
4Choose d such that (de)mod empty(119899)=1
5Public key is (en) and Private key is (dn)
6 If egt=2 then check i==1 if so return 1 else return 0
7In a iteration check for e(i)==1 if so take mod function of message with n
8Message is converted to cipher text in ASCII form with the key generated
9The cipher data in ASCII form is converted to binary form
33 2D Logistic Encryption
The chaotic system is a deterministic nonlinear system It possesses a varied
characteristics such as high sensitivity to initial conditions and system parameters
random-like behaviors and so forth Chaotic sequences produced by chaotic maps are
pseudo-random sequences their structures are very complex and difficult to be analyzed
and predicted In other words chaotic systems can improve the security of encryption
systems Thus it is advisable to encrypt digital image with chaotic systems There are
two chaotic maps one is logistic map and the other is 2D logistic map In the proposed
work 2D logistic map is used
Logistic map is an example for chaotic map and it is described as follows
x(n+1)=μ x(n)(1minusx(n))
μ is a positive constant sometimes known as the biotic potential gives the so-called
logistic map x(n) is series of values used to plot Logistic mapwhere μ isin [04] x(n) isin
(01) and n = 01 2hellip The research result shows that the system is in chaotic state
under the condition that 356994 lt μ le 4
18
2D logistic map is described in as follows
119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2
119910119894+1 = 1205832 lowast 119910119894(1 minus 119910119894) + 1205732((119909119894)2 + 119909119894 lowast 119910119894)
Where z(xy) is the logistic map 1205831 and 1205832 are positive constant sometimes known as the
biotic potential gives the so-called logistic map x be the position of images in x axis
and y be the position of images in y axis 1205731 and 1205732 are the correlation constantsWhen
275 lt μ1 le 34 275 lt μ2 le 345 015 lt 1205731le 021 and 013 lt 1205732 le 015 the system is in
chaotic state and can generate two chaotic sequences in the region (01] Due to the
system parameter γ1 and γ2 which have smaller value range we set γ1 = 017 and γ2 =
014 other parameters can be seen as secret keys
Algorithm
1A random key is generated in binary form ( 1times256) and it is stored in a array
2The random key generated is translated to map format using block processing (4times4)
3 The row and column wise transformation is carried out
4The key is now used to encrypt the cover image
52D logistic image undergoes substitution and permutation (column and row wise
shuffling is done)
34 DNA Sequence
A single DNA sequence is made up of four nucleic acid
bases A (adenine) C (cytosine) G (guanine) and T (thymine) where A and T are
complements and C and G are complements Let binary number 0 and 1 be
complements so 00 and 11 are complements and 01 and 10 are complements Thus we
can use these four bases A T G and C to encode 01 10 00 and 11 respectively The
encoding method still satisfies the Watson-Crick complement rule Usually each pixel
value of the 8 bit grey image can be expressed to 8 bits binary stream The binary stream
can be encoded to a DNA sequence whose length is 4 For example if the first pixel
19
value of the original image is 75 convert it into a binary stream [01001011] By using the
above DNA encoding rule to encode the stream we can get a DNA sequence [AGTC]
whereas we use A T G and C to express 01 10 00 and 11 respectively We can get a
binary sequence [01001011]
35 Attacks
The steganographic algorithm is used to embed secret messages into cover
image To obtain stego image while exchanging these stego-image through the public
communication channel various attacks have been made The are generally classified
into two types intentional or unintentional attacks Examples of unintentional attacks are
transmission errors lossy compression and changing the visual properties of the stego
document Intentional attacks on the other hand are deliberate attempts to distinguish
stego-objects from unmodified objects and thus detect the presence of covert
communication Attack methods generally exploit the fact that embedding information
usually changes the statistical properties of the objects compared to typical unmodified
objects In this proposed algorithm various attacks have been applied on the encrypted
image They are as follows
1)Shearing
2) Image Scaling
3) Image Rotating
4) Image color reduction
5) Image blurred
6) Image flip
7) cropping and intensity transformation
8) Image sharpening
9) Gaussian Noise and filtering
10) Image Contrast
11) Speckle Noise and Filtering
20
351 Shearing
The image is resized with the scale factor Resized image is rotated with
angle(theta)Finally spatial transformation from control point pairs is implemented
For example
Scale factor 09
Theta10
Fig 32 Shearing Image
352 Image Scaling
It resizes the image with a scale factor and rotation is performed It rotates the
image by angle (degrees) in a counterclockwise direction around its center point To
rotate the image clockwise specify a negative value for angle It makes the output image
large enough to contain the entire rotated image It uses nearest neighbour interpolation
setting the values of pixels in Output image that are outside the rotated image to 0 (zero)
For example
Scale Factor07
Theta30
scaling Image
21
Fig 33 Scaling Image
353 Rotation
It rotates the image by angle degrees in a counterclockwise direction around its
center point To rotate the image clockwise specify a negative value for angle It makes
the output image large enough to contain the entire rotated image It uses nearest
neighbour interpolation setting the values of pixels in Output image that are outside the
rotated image to 0 (zero)
For Example
Theta180
Fig 34 Rotation Image
354 Colour Reduced Image
It creates an indexed image approximation of the RGB image in the array RGB by
dithering the colors in the colormap map The colormap cannot have more than 65536
resized and rotated image
Rotated image
22
colors
For Example
Indexed image with 32 Colors
Fig 35 Colour Reduced Image
355 Blur Image
The image is blurred by using N-D filtering of multidimensional images It filters
the multidimensional array of original image with the multidimensional filter The array
of original image can be logical or a nonsparse numeric array of any class and dimension
The result image has the same size and class as of original image
Fig 36 Blur Image
Color reduced image
50 100 150 200 250 300 350 400 450 500
50
100
150
200
250
300
350
400
450
500
Blurred image
23
356 Flipped Image
It flips the image upside down Flipping is used to invert the image
Fig 37 Flipped Image
357 Cropped Image
It creates an interactive crop image tool associated with the image displayed in the
current figure called the target image The crop image tool is a movable resizable
rectangle that you can position interactively using the mouse When the crop image tool
is active the pointer changes to cross hairs when it is moved over the target image
Using the mouse image to be cropped can be specified by clicking and dragging the
mouse The crop rectangle using the mouse can be moved or resized When sizing and
positioning of the crop rectangle is finished create the cropped image by double-clicking
the left mouse button or by choosing crop image from the context menu Image cropping
returns the cropped image
Flipped image
24
Fig 38 Cropped Image
358 Intensity Transformation Adjust
It maps the intensity values in grayscale image to new values in resultant image
such that 1 of data is saturated at low and high intensity of original image This
increases the contrast of the output image
Fig 39 Intensity Transformation Image
Cropped Image
Intensity Transformation
25
359 Sharpening
Input array values outside the bounds of the array are assumed to equal the nearest
array border value The image is sharpened by using N-D filtering of multidimensional
images It filters the multidimensional array of original image with the multidimensional
filter The array of original image can be logical or a nonsparse numeric array of any
class and dimension The result image has the same size and class as of original image
Fig 310 Sharpened Image
3510 Gaussian Noise and Median Filtering
It adds Gaussian noise to the images Gaussian white noise have constant mean
and variance The noise added image is filtered using Median Filtering Median filtering
is a nonlinear operation often used in image processing to reduce salt and pepper noise
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Sharpened Image
26
Fig 311 Gaussian Noise and Median Filter Image
3511 Histogram of contrast image
It enhances the contrast of images by transforming the values in an intensity
image or the values in the colormap of an indexed image so that the histogram of the
output image approximately matches a specified histogram
Fig 312 Contrast Image
Gaussian Noise
Median Filtering
Contrast Image
27
Fig 313 Histogram of Contrast Image
3512 Speckle noise and Median Filtering
It adds multiplicative noise to the image I using the equation J = I+nI where n is
uniformly distributed random noise with mean 0 and variance v The default for v is 004
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Fig 314 Speckle Noise and Median Filter Image
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Histogram of Contrast Image
0 50 100 150 200 250
Speckle Noise
Median Filtering
28
36 Proposed Algorithm
Step1 Enter two keys a private key and a public key through which the RSA algorithm is
performed
Step2These key are used for encrypting the secret data using chaotic algorithm with
threshold of 2
Step3The secret data is converted into binary format from ASCII code
Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied
Step5The encryption is carried out with the key generated randomly in binary(1times256)
Step6 The 2D logistic substitution and permutation are carried out
Step7The resulting binary sequence is added with the encrypted text in LSB
Step8The image is converted to DNA sequence and transmitted
Step9 Various Attacks have been applied on the resultant image
Step10The inverse process is carried out to retrieve the original image and data
Step11The Performance Metrics have been calculated
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
2
1
CHAPTER 1
INTRODUCTION
In an information sharing environment security of information plays an important
role Some information that is sensitive or confidential in nature must be kept private
With the introduction of computers the need for automated tools for protecting files and
other information stored in the computer become evident Transmission of sensitive
information via an open internet channel increases the risk of interception There are
many techniques proposed to deal with this issue They are
1) Cryptography
2) Steganography
3) Reversible Data Hiding
11 CRYPTOGRAPHY
Cryptography is the practice and study of techniques for secure communication in
the presence of third parties (called adversaries) More generally it is about constructing
and analyzing protocols that overcome the influence of adversaries This technique alters
the form of the message at the sender and transmits it At the receiver the original
message is extracted It mainly involves 2 operations
Encryption It is the process of the conversion of information from a readable state to
apparent nonsense with the usage of a key It is done by the sender
Decryption It is the reverse process of encryption That is it is the process of converting
scrambled message into the original one with the help of key The key may be similar to
the one which is used in encryption or it may be a different one It is done at the receiver
side
The cryptography is characterized by 3 independent dimensions
2
1) The type of operations used for transforming Plaintext to Cipher text
All encryption algorithms are based on two general principles They are
substitution and transposition Substitution is the one in which each element in the plain
text is transformed into another element Transposition is the one in which elements in
the plain text are rearranged The fundamental condition is that no information be lost
2) The Number of keys used
Based on this we can classify the techniques into two
a) Symmetric-key Cryptography Symmetric-key cryptography refers to encryption
methods in which both the sender and receiver share the same key (or less commonly in
which their keys are different but related in an easily computable way)
Figure 11 Symmetric-key cryptography
b) Public key Cryptography In public-key cryptosystems the public key may be freely
distributed while its paired private key must remain secret In a public-key encryption
system the public key is used for encryption while the private or secret key is used for
decryption
Figure 12 Public key Cryptography
3
3) The way in which the plaintext is processed
There are 2 types
a) Block Cipher It processes the input one block of elements at a time producing an
output block for each input block
b) Stream Cipher It processes the input elements continuously producing output one
element at a time as it goes along
12 STEGANOGRAPHY
It is the art and science of encoding hidden messages in such a way that no one
apart from the sender and intended recipient suspects the existence of the message It is a
form of security through obscurity Generally the hidden messages will appear to be (or
be part of) something else images articles shopping lists or some other cover texts
Plainly visible encrypted messages no matter how unbreakable will arouse interest and
may in themselves be incriminating in countries where encryption is illegal For example
the hidden message may be in invisible ink between the visible lines of a private letter
The advantage of steganography over cryptography alone is that the intended secret
message does not attract attention to itself as an object of scrutiny So cryptography is the
practice of protecting the contents of a message alone steganography is concerned with
concealing the fact that a secret message is being sent as well as concealing the contents
of the message Steganography includes the concealment of information within computer
files In digital steganography electronic communications may include steganographic
coding inside of a transport layer such as a document file image file program or
protocol Media files are ideal for steganographic transmission because of their large size
There has been a rapid growth of interest in steganography for two main reasons
(i) The publishing and broadcasting industries have become interested in techniques for
hiding encrypted copyright marks and serial numbers in digital films audio
recordings books and multimedia products
(ii) Moves by various governments to restrict the availability of encryption services
have motivated people to study methods by which private messages can be
4
embedded in seemingly innocuous cover messages
Fig 13 Categories of Image Steganography
There are many applications for digital steganography of image including
copyright protection feature tagging and secret communication Copyright notice or
watermark can embedded inside an image to identify it as intellectual property If
someone attempts to use this image without permission we can prove by extracting the
watermark In feature tagging captions annotations time stamps and other descriptive
elements can be embedded inside an image Copying the stegondashimage also copies of the
embedded features and only parties who posses the decoding stego-key will be able to
extract and view the features On the other hand secret communication does not advertise
a covert communication by using steganography Therefore it can avoid scrutiny of the
sender message and recipient This is effective only if the hidden communication is not
detected by the others people In general steganography is two types reversible and
irreversible
5
13 Reversible Data Hiding
Figure 14 Reversible Data Hiding System
Secret Message The secret message or information to hide
Cover File Digital Medium The data or medium which concealed the secret message
Stego File A modified version of cover that contains the secret message
Key Additional secret data that is needed for the embedding and extracting processes
and must be known to both the sender and the recipient
Steganographic Method A steganographic function that takes cover secret message
and key as parameters and produces stego as output
Inverse of Steganographic Method A steganographic function that has stego and key
as parameters and produces secret message as output This is the inverse of method used
in embeding process in the sense that the result of the extracting process is identical to the
input of the embedding process
6
CHAPTER 2
LITERATURE SURVEY
1) Nidhi Sethi Deepika Sharma (2014) ldquoA Novel Method Of Image
Encryption using Logistic Mappingrdquo International Journal of Computer
Science Engineering (IJCSE)
This paper presents a new method to develop secure image-encryption techniques
using a logistics based encryption algorithm In this technique a Haar wavelet transform
was used to decompose the image and decorrelate its pixels into averaging and
differencing components The logistic based encryption algorithm produces a cipher of
the test image that has good diffusion and confusion properties The remaining
components (the differencing components) are compressed using a wavelet transform
Many test images are used to demonstrate the validity of the proposed algorithm The
results of several experiments show that the proposed algorithm for image cryptosystems
provides an efficient and secure approach to real-time image encryption and transmission
To send the keys in secure form steganography will be used Steganographic techniques
allow one party to communicate information to another party without a third party even
knowing that the communication is occurring
Advantages
(i) Efficient approach
(ii) Secure key transmission
(iii) Better image quality
7
2) Jing Wang Guoping Jiang (2013) ldquoA Self-Adaptive Parallel Encryption
Algorithm Based on Discrete 2D-Logistic Maprdquo International Journal of
Modern Nonlinear Theory and Application
A self-adaptive parallel encryption algorithm based on discrete 2D-Logistic
map is developed according to the position scrambling and diffusion of multi-direction in
variable space of spatial chaos The binary sequences b1b2b3bn are obtained according
to the user key in which the binary sequence 0 and 1 denote distribution mode of
processors and the number of binary sequence n denotes cycle number Then the
pseudorandom 2D matrix is generated by 2D-Logistic map and adaptive segmentation is
applied in image matrix and pseudorandom matrix according to the value and the number
of binary sequence The parallel operation is used among blocks to improve efficiency
and meet real-time demand in transmission processes However the pixel permutation is
applied in partitioned matrix through ergodic matrix generated by pseudo-random matrix-
block to decrease the correlation of adjacent pixels Then the pixel substitution is used for
fully diffusing through cipher block chaining mode until n cycles The proposed
algorithm can meet the three requirements of parallel operation in image encryption and
the real-time requirement in transmission processes The security is proved by theoretical
analysis and simulation results
Advantages
1Security is provided
2Effeciency is improved
8
3) LJani Anbarasi GSAnandha Mala Modigari Narendra (2014) ldquoDNA
based Multi-Secret Image Sharingrdquo International Conference on
Information and Communication Technologies (ICICT)
Multiple secret sharing algorithm using the YCH scheme combined with
DNA encoding is proposed focusing at better security Firstly DNA encoding for
multiple images is carried out then the addition of these encoded components by DNA is
performed Secondly the (t n) scheme used the Lagrange interpolation polynomial to
share these DNA scrambled matrices is performed bdquot‟ denotes threshold value and bdquon‟
denotes the number of participants The resulting scrambled images are encrypted into n
shares using Shamir‟s polynomial Thirdly these shares are embedded using a modular
operation Finally bdquot‟ or more shares are pooled which reconstructs the scrambled
matrices and by decoding the DNA scrambled matrices multiple secrets are
reconstructed without loss The simulation results and the security analysis prove that this
algorithm is perfect and produces results with better PSNR value The correlation co-
efficient shows that this also has the ability of resisting various attacks
Advantages
1Security is better
2Resistance against Attack
9
4)Della Babya Jitha Thomasa Gisny Augustinea Elsa Georgea Neenu
Rosia Michaela (2014) ldquoA Novel DWT based Image Securing Method using
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Steganography is a data hiding technique that is widely used in various
information securing applications Steganography transmits data by hiding the existence
of the message so that a viewer cannot identify the transmission of message and hence
not able to decrypt it This work proposes a data securing technique that is used for
hiding multiple color images into a single color image using the Discrete Wavelet
Transform The cover image is split up into R G and B planes Secret images are
embedded into these planes An N-level decomposition of the cover image and the secret
images are done and some frequency components of the same are combined Secret
images are then extracted from the stego image Here the stego image obtained has a less
perceptible changes compared to the original image with high overall security
Advantages
1Less perceptible changes
2Overall security is high
10
5)Prasenjit Dasa Subhrajyoti Deba Nirmalya Kara Baby
Bhattacharyaa(2014)ldquoAn Improved DNA based Dual Cover
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Dual cover steganography is an evolving technique in the field of covert
data transmission This paper focuses on the concept of using a theoretical single stranded
DNA (ssDNA) as a primary cover which is extracted from an inconspicuous cover
image They have analyzed the security loopholes and performance issues of the existing
algorithm and proposed an improved algorithm on the same basisthey use 2D Logistic
map for encrypting the cover imageThen overall encryption is RC43 types of encryption
is generally used Performance of both the algorithms are tested against several visual
and statistical attacks and parameterized in terms of both security and capacity The
comparison shows that the proposed improvements provide better overall security
Advantages
1 Robustness against various attack
2 Performance measure are calculated
3 Data hiding improves security
11
6) Bhavana Agrawal Himani Agrawal ( May-2013) ldquoImplementation of AES
and RSA Using Chaos systemrdquo International Journal of Scientific amp
Engineering Research Vol 4 No 5
This paper presents two cryptographic algorithm AES and RSA Using Chaos
Chaos has attracted much attention in the field of cryptography It describes a system
which is sensitive to initial condition It generates apparently random behavior but at the
same time is completely deterministic Chaos function is used to increase the complexity
and Security of the SystemAES and RSA are the two cryptographic algorithms In AES
we apply the Chaos on S-box where as in RSA we mix the plaintext with Chaos sequence
First then apply for encryption and decryption After Implementing AES and RSA they
compare both the technique on the basis of speed
Advantages
1Chaos function is used to improve complexity
2The speed has been improved with combined technique of AES and RSA along with
chaos technique
12
7) Pahrul Irfan Yudi Prayudi Imam Riadi ( August 2015) ldquo Image
Encryption using Combination of Chaotic System and Rivers Shamir
Adleman (RSA)rdquo International Journal of Computer Applications Vol 123
No6
Security and confidentiality of data or information at the present time has
become an important concern Advanced methods for secure transmission storage and
retrieval of digital images are increasingly needed for a number of military medical
homeland security and other applications Various kinds of techniques for increase
security data or information already is developed one common way is by cryptographic
techniques Cryptography is science to maintain the security of the message by changing
data or information into a different form so the message cannot be recognized To
compensate for increasing computing speeds increases it takes more than one encryption
algorithm to improve security of digital images One way is by using algorithms to
double cryptography do encryption and decryption Cryptographic algorithm often used
today and the proven strength specially the digital image is Algorithm with Chaos
system To improve security at the image then we use Additional algorithms namely
Rivers algorithm Shamir Adleman (RSA) which known as the standard of cryptography
algorithms This research aims to optimize security bitmap image format by combining
the two algorithms namely Chaos-based algorithms and RSA algorithm into one
application Experiments conducted show that the proposed algorithm possesses robust
security features such as fairly uniform distribution high sensitivity to both keys and
plain images almost ideal entropy and the ability to highly de-correlate adjacent pixels
in the cipher images Furthermore it has a large key space and transform image to pure
text file which greatly increases its security for image encryption
Advantages
1 It aims to optimize security bitmap image format by combining the two algorithms
namely Chaos-based algorithms and RSA algorithm into one application
13
8) Weiming Zhang KedeMa NenghaiYu(2013) ldquo Reversibility improved
data hiding in encrypted imagesrdquo School of Information Science and
Technology
A novel reversible data hiding technique in encrypted images is presented in this
paper Instead of embedding data in encrypted images directly some pixels are estimated
before encryption so that additional data can be embedded in the estimating errors A
bench mark encryption algorithm (eg AES) is applied to the rest pixels of the image and
a special encryption scheme is designed to encrypt the estimating errors Without the
encryption key one cannot get access to the original image However provided with the
data hiding key only he can embed in or extract from the encrypted image additional data
without knowledge about the original image Moreover the data extraction and image
recovery are free of errors for all images Experiments demonstrate the feasibility and
efficiency of the proposed method especially in aspect of embedding rate versus Peak
Signal-to-Noise Ratio (PSNR)
The paper proposes a novel method to significantly improve the performance by
reversing the order of encryption and vacating room In the light of this idea we empty
out room prior to image encryption by shifting the histogram of estimating errors of some
pixels and the emptied out room will be used for data hiding The proposed method is
composed of four primary steps vacating room and encrypting image data hiding in the
encrypted image data extraction and image recovery Two different schemes extraction
before decryption and decryption before extraction are raised to cope with different
applications
Advantages
(i) Achieves excellent performance in three aspects complete reversibility PSNR
under given embedding rate separability between data higher extraction and
image decryption
14
CHAPTER 3
PROPOSED METHODOLOGY
The proposed data hiding scheme aims at the security of the hidden data
Embedding is performed in spatial domain The data to be embedded is converted into
binary form from ASCII code using chaos encryption and is embedded into the cover
image obtained after 2D logistic map This embedded image is secured using symmetric
key (K1)They are converted into DNA sequence to provide additional level of security
The hidden data can be extracted from the cover image accurately with the help of
decryption techniques and secret key (K1) The cover image can also be extracted
without any distortion The fig 31 shows the workflow
Fig 31 Work Flow Diagram
SECRET DATA
COVER IMAGE
CHAOTIC
ENCRYPTION
ENCRY 2D LOGISTIC
ENCRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
CHAOTIC
DECRYPTION
ENCRY
SECRET DATA
COVER IMAGE 2D LOGISTIC
DECRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
15
31 Chaotic Encryption
Chaotic cryptography is the application of the mathematical chaos theory to the
practice of the cryptography the study or techniques used to privately and securely
transmit information with the presence of an third-party or adversary The use of chaos
or randomness in cryptography has long been sought after by entities wanting a new way
to encrypt messages However because of the lack of thorough provable security
properties and low acceptable performance chaotic cryptography has encountered
setbacksIn order to use chaos theory acceptably in cryptography they must first be
mapped to each other Properties in chaotic systems and cryptographic primitives share
unique characteristics that allow for the chaotic systems to be applied to cryptography If
chaotic parameters as well as cryptographic keys can be mapped symmetrically or
mapped to produce acceptable and functional outputs it will make it next to impossible
for an adversary to find the outputs without any knowledge the initial values Since
chaotic maps in a real life scenario require a set of numbers that are limited they may in
fact have no real purpose in a cryptosystem if the chaotic behavior can be predicted To
counter this possibility there exists simple to advanced ciphers Chaos theory used in
cryptosystems for commercial implementation has proven to be unsuccessful mainly
because a chaos theories‟ requirement to use intervals of real numbers Given enough
resources and time an adversary could be able to predict functional outcomes Since
chaotic cryptosystems have no root in number theory this would make it difficult or
impossible to implement therefore impractical
32 The RSA Algorithm
The RSA cryptosystem named after its inventors R Rivest A Shamir and L
Adleman is the most widely used public key Cryptosystem It may be used to provide
both secrecy and digital signatures and its security is based on the intractability of the
integer factorizationThe RSA algorithm involves three steps key generation encryption
and decryption
16
321 Key Generation
RSA involves a public key and a private key The public key can be known to
everyone and is used for encrypting messages Messages encrypted with the public key
can only be decrypted in a reasonable amount of time using the private key The keys for
the RSA algorithm are generated the following way To generate the two keys choose
two random large prime numbers p and q For maximum security choose p and q of
equal length Then randomly choose the encryption key e such that e and ( p minus1) (q minus1)
are relatively prime Finally use the extended Euclidean algorithm to compute the
decryption key d such that
d= e-1
mod ( (p-1) (q-1))
Note that d and n are also relatively prime The numbers e and K are the public
key the number d is the private key The two primes p and q are no longer needed They
should be discarded but never revealed
322 Encryption
Firstly receiver transmits her public key (n e) to sender and keeps the private key
secret If sender wishes to send message M to receiver Sender change the message M in
to integer m such that 0 le mltn Then sender computes the cipher text c corresponding to
Cequiv me
(mod n)
323 Decryption
Receiver can recover M from c by using private key exponent d via computing
M equiv cd
(mod n)
17
Algorithm
1Select any two prime numbers say (pq)
2Compute n=pq and also compute empty(119899)=(p-1)(q-1)
3Choose e such that 1ltelt empty(119899)
4Choose d such that (de)mod empty(119899)=1
5Public key is (en) and Private key is (dn)
6 If egt=2 then check i==1 if so return 1 else return 0
7In a iteration check for e(i)==1 if so take mod function of message with n
8Message is converted to cipher text in ASCII form with the key generated
9The cipher data in ASCII form is converted to binary form
33 2D Logistic Encryption
The chaotic system is a deterministic nonlinear system It possesses a varied
characteristics such as high sensitivity to initial conditions and system parameters
random-like behaviors and so forth Chaotic sequences produced by chaotic maps are
pseudo-random sequences their structures are very complex and difficult to be analyzed
and predicted In other words chaotic systems can improve the security of encryption
systems Thus it is advisable to encrypt digital image with chaotic systems There are
two chaotic maps one is logistic map and the other is 2D logistic map In the proposed
work 2D logistic map is used
Logistic map is an example for chaotic map and it is described as follows
x(n+1)=μ x(n)(1minusx(n))
μ is a positive constant sometimes known as the biotic potential gives the so-called
logistic map x(n) is series of values used to plot Logistic mapwhere μ isin [04] x(n) isin
(01) and n = 01 2hellip The research result shows that the system is in chaotic state
under the condition that 356994 lt μ le 4
18
2D logistic map is described in as follows
119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2
119910119894+1 = 1205832 lowast 119910119894(1 minus 119910119894) + 1205732((119909119894)2 + 119909119894 lowast 119910119894)
Where z(xy) is the logistic map 1205831 and 1205832 are positive constant sometimes known as the
biotic potential gives the so-called logistic map x be the position of images in x axis
and y be the position of images in y axis 1205731 and 1205732 are the correlation constantsWhen
275 lt μ1 le 34 275 lt μ2 le 345 015 lt 1205731le 021 and 013 lt 1205732 le 015 the system is in
chaotic state and can generate two chaotic sequences in the region (01] Due to the
system parameter γ1 and γ2 which have smaller value range we set γ1 = 017 and γ2 =
014 other parameters can be seen as secret keys
Algorithm
1A random key is generated in binary form ( 1times256) and it is stored in a array
2The random key generated is translated to map format using block processing (4times4)
3 The row and column wise transformation is carried out
4The key is now used to encrypt the cover image
52D logistic image undergoes substitution and permutation (column and row wise
shuffling is done)
34 DNA Sequence
A single DNA sequence is made up of four nucleic acid
bases A (adenine) C (cytosine) G (guanine) and T (thymine) where A and T are
complements and C and G are complements Let binary number 0 and 1 be
complements so 00 and 11 are complements and 01 and 10 are complements Thus we
can use these four bases A T G and C to encode 01 10 00 and 11 respectively The
encoding method still satisfies the Watson-Crick complement rule Usually each pixel
value of the 8 bit grey image can be expressed to 8 bits binary stream The binary stream
can be encoded to a DNA sequence whose length is 4 For example if the first pixel
19
value of the original image is 75 convert it into a binary stream [01001011] By using the
above DNA encoding rule to encode the stream we can get a DNA sequence [AGTC]
whereas we use A T G and C to express 01 10 00 and 11 respectively We can get a
binary sequence [01001011]
35 Attacks
The steganographic algorithm is used to embed secret messages into cover
image To obtain stego image while exchanging these stego-image through the public
communication channel various attacks have been made The are generally classified
into two types intentional or unintentional attacks Examples of unintentional attacks are
transmission errors lossy compression and changing the visual properties of the stego
document Intentional attacks on the other hand are deliberate attempts to distinguish
stego-objects from unmodified objects and thus detect the presence of covert
communication Attack methods generally exploit the fact that embedding information
usually changes the statistical properties of the objects compared to typical unmodified
objects In this proposed algorithm various attacks have been applied on the encrypted
image They are as follows
1)Shearing
2) Image Scaling
3) Image Rotating
4) Image color reduction
5) Image blurred
6) Image flip
7) cropping and intensity transformation
8) Image sharpening
9) Gaussian Noise and filtering
10) Image Contrast
11) Speckle Noise and Filtering
20
351 Shearing
The image is resized with the scale factor Resized image is rotated with
angle(theta)Finally spatial transformation from control point pairs is implemented
For example
Scale factor 09
Theta10
Fig 32 Shearing Image
352 Image Scaling
It resizes the image with a scale factor and rotation is performed It rotates the
image by angle (degrees) in a counterclockwise direction around its center point To
rotate the image clockwise specify a negative value for angle It makes the output image
large enough to contain the entire rotated image It uses nearest neighbour interpolation
setting the values of pixels in Output image that are outside the rotated image to 0 (zero)
For example
Scale Factor07
Theta30
scaling Image
21
Fig 33 Scaling Image
353 Rotation
It rotates the image by angle degrees in a counterclockwise direction around its
center point To rotate the image clockwise specify a negative value for angle It makes
the output image large enough to contain the entire rotated image It uses nearest
neighbour interpolation setting the values of pixels in Output image that are outside the
rotated image to 0 (zero)
For Example
Theta180
Fig 34 Rotation Image
354 Colour Reduced Image
It creates an indexed image approximation of the RGB image in the array RGB by
dithering the colors in the colormap map The colormap cannot have more than 65536
resized and rotated image
Rotated image
22
colors
For Example
Indexed image with 32 Colors
Fig 35 Colour Reduced Image
355 Blur Image
The image is blurred by using N-D filtering of multidimensional images It filters
the multidimensional array of original image with the multidimensional filter The array
of original image can be logical or a nonsparse numeric array of any class and dimension
The result image has the same size and class as of original image
Fig 36 Blur Image
Color reduced image
50 100 150 200 250 300 350 400 450 500
50
100
150
200
250
300
350
400
450
500
Blurred image
23
356 Flipped Image
It flips the image upside down Flipping is used to invert the image
Fig 37 Flipped Image
357 Cropped Image
It creates an interactive crop image tool associated with the image displayed in the
current figure called the target image The crop image tool is a movable resizable
rectangle that you can position interactively using the mouse When the crop image tool
is active the pointer changes to cross hairs when it is moved over the target image
Using the mouse image to be cropped can be specified by clicking and dragging the
mouse The crop rectangle using the mouse can be moved or resized When sizing and
positioning of the crop rectangle is finished create the cropped image by double-clicking
the left mouse button or by choosing crop image from the context menu Image cropping
returns the cropped image
Flipped image
24
Fig 38 Cropped Image
358 Intensity Transformation Adjust
It maps the intensity values in grayscale image to new values in resultant image
such that 1 of data is saturated at low and high intensity of original image This
increases the contrast of the output image
Fig 39 Intensity Transformation Image
Cropped Image
Intensity Transformation
25
359 Sharpening
Input array values outside the bounds of the array are assumed to equal the nearest
array border value The image is sharpened by using N-D filtering of multidimensional
images It filters the multidimensional array of original image with the multidimensional
filter The array of original image can be logical or a nonsparse numeric array of any
class and dimension The result image has the same size and class as of original image
Fig 310 Sharpened Image
3510 Gaussian Noise and Median Filtering
It adds Gaussian noise to the images Gaussian white noise have constant mean
and variance The noise added image is filtered using Median Filtering Median filtering
is a nonlinear operation often used in image processing to reduce salt and pepper noise
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Sharpened Image
26
Fig 311 Gaussian Noise and Median Filter Image
3511 Histogram of contrast image
It enhances the contrast of images by transforming the values in an intensity
image or the values in the colormap of an indexed image so that the histogram of the
output image approximately matches a specified histogram
Fig 312 Contrast Image
Gaussian Noise
Median Filtering
Contrast Image
27
Fig 313 Histogram of Contrast Image
3512 Speckle noise and Median Filtering
It adds multiplicative noise to the image I using the equation J = I+nI where n is
uniformly distributed random noise with mean 0 and variance v The default for v is 004
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Fig 314 Speckle Noise and Median Filter Image
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Histogram of Contrast Image
0 50 100 150 200 250
Speckle Noise
Median Filtering
28
36 Proposed Algorithm
Step1 Enter two keys a private key and a public key through which the RSA algorithm is
performed
Step2These key are used for encrypting the secret data using chaotic algorithm with
threshold of 2
Step3The secret data is converted into binary format from ASCII code
Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied
Step5The encryption is carried out with the key generated randomly in binary(1times256)
Step6 The 2D logistic substitution and permutation are carried out
Step7The resulting binary sequence is added with the encrypted text in LSB
Step8The image is converted to DNA sequence and transmitted
Step9 Various Attacks have been applied on the resultant image
Step10The inverse process is carried out to retrieve the original image and data
Step11The Performance Metrics have been calculated
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
1
CHAPTER 1
INTRODUCTION
In an information sharing environment security of information plays an important
role Some information that is sensitive or confidential in nature must be kept private
With the introduction of computers the need for automated tools for protecting files and
other information stored in the computer become evident Transmission of sensitive
information via an open internet channel increases the risk of interception There are
many techniques proposed to deal with this issue They are
1) Cryptography
2) Steganography
3) Reversible Data Hiding
11 CRYPTOGRAPHY
Cryptography is the practice and study of techniques for secure communication in
the presence of third parties (called adversaries) More generally it is about constructing
and analyzing protocols that overcome the influence of adversaries This technique alters
the form of the message at the sender and transmits it At the receiver the original
message is extracted It mainly involves 2 operations
Encryption It is the process of the conversion of information from a readable state to
apparent nonsense with the usage of a key It is done by the sender
Decryption It is the reverse process of encryption That is it is the process of converting
scrambled message into the original one with the help of key The key may be similar to
the one which is used in encryption or it may be a different one It is done at the receiver
side
The cryptography is characterized by 3 independent dimensions
2
1) The type of operations used for transforming Plaintext to Cipher text
All encryption algorithms are based on two general principles They are
substitution and transposition Substitution is the one in which each element in the plain
text is transformed into another element Transposition is the one in which elements in
the plain text are rearranged The fundamental condition is that no information be lost
2) The Number of keys used
Based on this we can classify the techniques into two
a) Symmetric-key Cryptography Symmetric-key cryptography refers to encryption
methods in which both the sender and receiver share the same key (or less commonly in
which their keys are different but related in an easily computable way)
Figure 11 Symmetric-key cryptography
b) Public key Cryptography In public-key cryptosystems the public key may be freely
distributed while its paired private key must remain secret In a public-key encryption
system the public key is used for encryption while the private or secret key is used for
decryption
Figure 12 Public key Cryptography
3
3) The way in which the plaintext is processed
There are 2 types
a) Block Cipher It processes the input one block of elements at a time producing an
output block for each input block
b) Stream Cipher It processes the input elements continuously producing output one
element at a time as it goes along
12 STEGANOGRAPHY
It is the art and science of encoding hidden messages in such a way that no one
apart from the sender and intended recipient suspects the existence of the message It is a
form of security through obscurity Generally the hidden messages will appear to be (or
be part of) something else images articles shopping lists or some other cover texts
Plainly visible encrypted messages no matter how unbreakable will arouse interest and
may in themselves be incriminating in countries where encryption is illegal For example
the hidden message may be in invisible ink between the visible lines of a private letter
The advantage of steganography over cryptography alone is that the intended secret
message does not attract attention to itself as an object of scrutiny So cryptography is the
practice of protecting the contents of a message alone steganography is concerned with
concealing the fact that a secret message is being sent as well as concealing the contents
of the message Steganography includes the concealment of information within computer
files In digital steganography electronic communications may include steganographic
coding inside of a transport layer such as a document file image file program or
protocol Media files are ideal for steganographic transmission because of their large size
There has been a rapid growth of interest in steganography for two main reasons
(i) The publishing and broadcasting industries have become interested in techniques for
hiding encrypted copyright marks and serial numbers in digital films audio
recordings books and multimedia products
(ii) Moves by various governments to restrict the availability of encryption services
have motivated people to study methods by which private messages can be
4
embedded in seemingly innocuous cover messages
Fig 13 Categories of Image Steganography
There are many applications for digital steganography of image including
copyright protection feature tagging and secret communication Copyright notice or
watermark can embedded inside an image to identify it as intellectual property If
someone attempts to use this image without permission we can prove by extracting the
watermark In feature tagging captions annotations time stamps and other descriptive
elements can be embedded inside an image Copying the stegondashimage also copies of the
embedded features and only parties who posses the decoding stego-key will be able to
extract and view the features On the other hand secret communication does not advertise
a covert communication by using steganography Therefore it can avoid scrutiny of the
sender message and recipient This is effective only if the hidden communication is not
detected by the others people In general steganography is two types reversible and
irreversible
5
13 Reversible Data Hiding
Figure 14 Reversible Data Hiding System
Secret Message The secret message or information to hide
Cover File Digital Medium The data or medium which concealed the secret message
Stego File A modified version of cover that contains the secret message
Key Additional secret data that is needed for the embedding and extracting processes
and must be known to both the sender and the recipient
Steganographic Method A steganographic function that takes cover secret message
and key as parameters and produces stego as output
Inverse of Steganographic Method A steganographic function that has stego and key
as parameters and produces secret message as output This is the inverse of method used
in embeding process in the sense that the result of the extracting process is identical to the
input of the embedding process
6
CHAPTER 2
LITERATURE SURVEY
1) Nidhi Sethi Deepika Sharma (2014) ldquoA Novel Method Of Image
Encryption using Logistic Mappingrdquo International Journal of Computer
Science Engineering (IJCSE)
This paper presents a new method to develop secure image-encryption techniques
using a logistics based encryption algorithm In this technique a Haar wavelet transform
was used to decompose the image and decorrelate its pixels into averaging and
differencing components The logistic based encryption algorithm produces a cipher of
the test image that has good diffusion and confusion properties The remaining
components (the differencing components) are compressed using a wavelet transform
Many test images are used to demonstrate the validity of the proposed algorithm The
results of several experiments show that the proposed algorithm for image cryptosystems
provides an efficient and secure approach to real-time image encryption and transmission
To send the keys in secure form steganography will be used Steganographic techniques
allow one party to communicate information to another party without a third party even
knowing that the communication is occurring
Advantages
(i) Efficient approach
(ii) Secure key transmission
(iii) Better image quality
7
2) Jing Wang Guoping Jiang (2013) ldquoA Self-Adaptive Parallel Encryption
Algorithm Based on Discrete 2D-Logistic Maprdquo International Journal of
Modern Nonlinear Theory and Application
A self-adaptive parallel encryption algorithm based on discrete 2D-Logistic
map is developed according to the position scrambling and diffusion of multi-direction in
variable space of spatial chaos The binary sequences b1b2b3bn are obtained according
to the user key in which the binary sequence 0 and 1 denote distribution mode of
processors and the number of binary sequence n denotes cycle number Then the
pseudorandom 2D matrix is generated by 2D-Logistic map and adaptive segmentation is
applied in image matrix and pseudorandom matrix according to the value and the number
of binary sequence The parallel operation is used among blocks to improve efficiency
and meet real-time demand in transmission processes However the pixel permutation is
applied in partitioned matrix through ergodic matrix generated by pseudo-random matrix-
block to decrease the correlation of adjacent pixels Then the pixel substitution is used for
fully diffusing through cipher block chaining mode until n cycles The proposed
algorithm can meet the three requirements of parallel operation in image encryption and
the real-time requirement in transmission processes The security is proved by theoretical
analysis and simulation results
Advantages
1Security is provided
2Effeciency is improved
8
3) LJani Anbarasi GSAnandha Mala Modigari Narendra (2014) ldquoDNA
based Multi-Secret Image Sharingrdquo International Conference on
Information and Communication Technologies (ICICT)
Multiple secret sharing algorithm using the YCH scheme combined with
DNA encoding is proposed focusing at better security Firstly DNA encoding for
multiple images is carried out then the addition of these encoded components by DNA is
performed Secondly the (t n) scheme used the Lagrange interpolation polynomial to
share these DNA scrambled matrices is performed bdquot‟ denotes threshold value and bdquon‟
denotes the number of participants The resulting scrambled images are encrypted into n
shares using Shamir‟s polynomial Thirdly these shares are embedded using a modular
operation Finally bdquot‟ or more shares are pooled which reconstructs the scrambled
matrices and by decoding the DNA scrambled matrices multiple secrets are
reconstructed without loss The simulation results and the security analysis prove that this
algorithm is perfect and produces results with better PSNR value The correlation co-
efficient shows that this also has the ability of resisting various attacks
Advantages
1Security is better
2Resistance against Attack
9
4)Della Babya Jitha Thomasa Gisny Augustinea Elsa Georgea Neenu
Rosia Michaela (2014) ldquoA Novel DWT based Image Securing Method using
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Steganography is a data hiding technique that is widely used in various
information securing applications Steganography transmits data by hiding the existence
of the message so that a viewer cannot identify the transmission of message and hence
not able to decrypt it This work proposes a data securing technique that is used for
hiding multiple color images into a single color image using the Discrete Wavelet
Transform The cover image is split up into R G and B planes Secret images are
embedded into these planes An N-level decomposition of the cover image and the secret
images are done and some frequency components of the same are combined Secret
images are then extracted from the stego image Here the stego image obtained has a less
perceptible changes compared to the original image with high overall security
Advantages
1Less perceptible changes
2Overall security is high
10
5)Prasenjit Dasa Subhrajyoti Deba Nirmalya Kara Baby
Bhattacharyaa(2014)ldquoAn Improved DNA based Dual Cover
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Dual cover steganography is an evolving technique in the field of covert
data transmission This paper focuses on the concept of using a theoretical single stranded
DNA (ssDNA) as a primary cover which is extracted from an inconspicuous cover
image They have analyzed the security loopholes and performance issues of the existing
algorithm and proposed an improved algorithm on the same basisthey use 2D Logistic
map for encrypting the cover imageThen overall encryption is RC43 types of encryption
is generally used Performance of both the algorithms are tested against several visual
and statistical attacks and parameterized in terms of both security and capacity The
comparison shows that the proposed improvements provide better overall security
Advantages
1 Robustness against various attack
2 Performance measure are calculated
3 Data hiding improves security
11
6) Bhavana Agrawal Himani Agrawal ( May-2013) ldquoImplementation of AES
and RSA Using Chaos systemrdquo International Journal of Scientific amp
Engineering Research Vol 4 No 5
This paper presents two cryptographic algorithm AES and RSA Using Chaos
Chaos has attracted much attention in the field of cryptography It describes a system
which is sensitive to initial condition It generates apparently random behavior but at the
same time is completely deterministic Chaos function is used to increase the complexity
and Security of the SystemAES and RSA are the two cryptographic algorithms In AES
we apply the Chaos on S-box where as in RSA we mix the plaintext with Chaos sequence
First then apply for encryption and decryption After Implementing AES and RSA they
compare both the technique on the basis of speed
Advantages
1Chaos function is used to improve complexity
2The speed has been improved with combined technique of AES and RSA along with
chaos technique
12
7) Pahrul Irfan Yudi Prayudi Imam Riadi ( August 2015) ldquo Image
Encryption using Combination of Chaotic System and Rivers Shamir
Adleman (RSA)rdquo International Journal of Computer Applications Vol 123
No6
Security and confidentiality of data or information at the present time has
become an important concern Advanced methods for secure transmission storage and
retrieval of digital images are increasingly needed for a number of military medical
homeland security and other applications Various kinds of techniques for increase
security data or information already is developed one common way is by cryptographic
techniques Cryptography is science to maintain the security of the message by changing
data or information into a different form so the message cannot be recognized To
compensate for increasing computing speeds increases it takes more than one encryption
algorithm to improve security of digital images One way is by using algorithms to
double cryptography do encryption and decryption Cryptographic algorithm often used
today and the proven strength specially the digital image is Algorithm with Chaos
system To improve security at the image then we use Additional algorithms namely
Rivers algorithm Shamir Adleman (RSA) which known as the standard of cryptography
algorithms This research aims to optimize security bitmap image format by combining
the two algorithms namely Chaos-based algorithms and RSA algorithm into one
application Experiments conducted show that the proposed algorithm possesses robust
security features such as fairly uniform distribution high sensitivity to both keys and
plain images almost ideal entropy and the ability to highly de-correlate adjacent pixels
in the cipher images Furthermore it has a large key space and transform image to pure
text file which greatly increases its security for image encryption
Advantages
1 It aims to optimize security bitmap image format by combining the two algorithms
namely Chaos-based algorithms and RSA algorithm into one application
13
8) Weiming Zhang KedeMa NenghaiYu(2013) ldquo Reversibility improved
data hiding in encrypted imagesrdquo School of Information Science and
Technology
A novel reversible data hiding technique in encrypted images is presented in this
paper Instead of embedding data in encrypted images directly some pixels are estimated
before encryption so that additional data can be embedded in the estimating errors A
bench mark encryption algorithm (eg AES) is applied to the rest pixels of the image and
a special encryption scheme is designed to encrypt the estimating errors Without the
encryption key one cannot get access to the original image However provided with the
data hiding key only he can embed in or extract from the encrypted image additional data
without knowledge about the original image Moreover the data extraction and image
recovery are free of errors for all images Experiments demonstrate the feasibility and
efficiency of the proposed method especially in aspect of embedding rate versus Peak
Signal-to-Noise Ratio (PSNR)
The paper proposes a novel method to significantly improve the performance by
reversing the order of encryption and vacating room In the light of this idea we empty
out room prior to image encryption by shifting the histogram of estimating errors of some
pixels and the emptied out room will be used for data hiding The proposed method is
composed of four primary steps vacating room and encrypting image data hiding in the
encrypted image data extraction and image recovery Two different schemes extraction
before decryption and decryption before extraction are raised to cope with different
applications
Advantages
(i) Achieves excellent performance in three aspects complete reversibility PSNR
under given embedding rate separability between data higher extraction and
image decryption
14
CHAPTER 3
PROPOSED METHODOLOGY
The proposed data hiding scheme aims at the security of the hidden data
Embedding is performed in spatial domain The data to be embedded is converted into
binary form from ASCII code using chaos encryption and is embedded into the cover
image obtained after 2D logistic map This embedded image is secured using symmetric
key (K1)They are converted into DNA sequence to provide additional level of security
The hidden data can be extracted from the cover image accurately with the help of
decryption techniques and secret key (K1) The cover image can also be extracted
without any distortion The fig 31 shows the workflow
Fig 31 Work Flow Diagram
SECRET DATA
COVER IMAGE
CHAOTIC
ENCRYPTION
ENCRY 2D LOGISTIC
ENCRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
CHAOTIC
DECRYPTION
ENCRY
SECRET DATA
COVER IMAGE 2D LOGISTIC
DECRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
15
31 Chaotic Encryption
Chaotic cryptography is the application of the mathematical chaos theory to the
practice of the cryptography the study or techniques used to privately and securely
transmit information with the presence of an third-party or adversary The use of chaos
or randomness in cryptography has long been sought after by entities wanting a new way
to encrypt messages However because of the lack of thorough provable security
properties and low acceptable performance chaotic cryptography has encountered
setbacksIn order to use chaos theory acceptably in cryptography they must first be
mapped to each other Properties in chaotic systems and cryptographic primitives share
unique characteristics that allow for the chaotic systems to be applied to cryptography If
chaotic parameters as well as cryptographic keys can be mapped symmetrically or
mapped to produce acceptable and functional outputs it will make it next to impossible
for an adversary to find the outputs without any knowledge the initial values Since
chaotic maps in a real life scenario require a set of numbers that are limited they may in
fact have no real purpose in a cryptosystem if the chaotic behavior can be predicted To
counter this possibility there exists simple to advanced ciphers Chaos theory used in
cryptosystems for commercial implementation has proven to be unsuccessful mainly
because a chaos theories‟ requirement to use intervals of real numbers Given enough
resources and time an adversary could be able to predict functional outcomes Since
chaotic cryptosystems have no root in number theory this would make it difficult or
impossible to implement therefore impractical
32 The RSA Algorithm
The RSA cryptosystem named after its inventors R Rivest A Shamir and L
Adleman is the most widely used public key Cryptosystem It may be used to provide
both secrecy and digital signatures and its security is based on the intractability of the
integer factorizationThe RSA algorithm involves three steps key generation encryption
and decryption
16
321 Key Generation
RSA involves a public key and a private key The public key can be known to
everyone and is used for encrypting messages Messages encrypted with the public key
can only be decrypted in a reasonable amount of time using the private key The keys for
the RSA algorithm are generated the following way To generate the two keys choose
two random large prime numbers p and q For maximum security choose p and q of
equal length Then randomly choose the encryption key e such that e and ( p minus1) (q minus1)
are relatively prime Finally use the extended Euclidean algorithm to compute the
decryption key d such that
d= e-1
mod ( (p-1) (q-1))
Note that d and n are also relatively prime The numbers e and K are the public
key the number d is the private key The two primes p and q are no longer needed They
should be discarded but never revealed
322 Encryption
Firstly receiver transmits her public key (n e) to sender and keeps the private key
secret If sender wishes to send message M to receiver Sender change the message M in
to integer m such that 0 le mltn Then sender computes the cipher text c corresponding to
Cequiv me
(mod n)
323 Decryption
Receiver can recover M from c by using private key exponent d via computing
M equiv cd
(mod n)
17
Algorithm
1Select any two prime numbers say (pq)
2Compute n=pq and also compute empty(119899)=(p-1)(q-1)
3Choose e such that 1ltelt empty(119899)
4Choose d such that (de)mod empty(119899)=1
5Public key is (en) and Private key is (dn)
6 If egt=2 then check i==1 if so return 1 else return 0
7In a iteration check for e(i)==1 if so take mod function of message with n
8Message is converted to cipher text in ASCII form with the key generated
9The cipher data in ASCII form is converted to binary form
33 2D Logistic Encryption
The chaotic system is a deterministic nonlinear system It possesses a varied
characteristics such as high sensitivity to initial conditions and system parameters
random-like behaviors and so forth Chaotic sequences produced by chaotic maps are
pseudo-random sequences their structures are very complex and difficult to be analyzed
and predicted In other words chaotic systems can improve the security of encryption
systems Thus it is advisable to encrypt digital image with chaotic systems There are
two chaotic maps one is logistic map and the other is 2D logistic map In the proposed
work 2D logistic map is used
Logistic map is an example for chaotic map and it is described as follows
x(n+1)=μ x(n)(1minusx(n))
μ is a positive constant sometimes known as the biotic potential gives the so-called
logistic map x(n) is series of values used to plot Logistic mapwhere μ isin [04] x(n) isin
(01) and n = 01 2hellip The research result shows that the system is in chaotic state
under the condition that 356994 lt μ le 4
18
2D logistic map is described in as follows
119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2
119910119894+1 = 1205832 lowast 119910119894(1 minus 119910119894) + 1205732((119909119894)2 + 119909119894 lowast 119910119894)
Where z(xy) is the logistic map 1205831 and 1205832 are positive constant sometimes known as the
biotic potential gives the so-called logistic map x be the position of images in x axis
and y be the position of images in y axis 1205731 and 1205732 are the correlation constantsWhen
275 lt μ1 le 34 275 lt μ2 le 345 015 lt 1205731le 021 and 013 lt 1205732 le 015 the system is in
chaotic state and can generate two chaotic sequences in the region (01] Due to the
system parameter γ1 and γ2 which have smaller value range we set γ1 = 017 and γ2 =
014 other parameters can be seen as secret keys
Algorithm
1A random key is generated in binary form ( 1times256) and it is stored in a array
2The random key generated is translated to map format using block processing (4times4)
3 The row and column wise transformation is carried out
4The key is now used to encrypt the cover image
52D logistic image undergoes substitution and permutation (column and row wise
shuffling is done)
34 DNA Sequence
A single DNA sequence is made up of four nucleic acid
bases A (adenine) C (cytosine) G (guanine) and T (thymine) where A and T are
complements and C and G are complements Let binary number 0 and 1 be
complements so 00 and 11 are complements and 01 and 10 are complements Thus we
can use these four bases A T G and C to encode 01 10 00 and 11 respectively The
encoding method still satisfies the Watson-Crick complement rule Usually each pixel
value of the 8 bit grey image can be expressed to 8 bits binary stream The binary stream
can be encoded to a DNA sequence whose length is 4 For example if the first pixel
19
value of the original image is 75 convert it into a binary stream [01001011] By using the
above DNA encoding rule to encode the stream we can get a DNA sequence [AGTC]
whereas we use A T G and C to express 01 10 00 and 11 respectively We can get a
binary sequence [01001011]
35 Attacks
The steganographic algorithm is used to embed secret messages into cover
image To obtain stego image while exchanging these stego-image through the public
communication channel various attacks have been made The are generally classified
into two types intentional or unintentional attacks Examples of unintentional attacks are
transmission errors lossy compression and changing the visual properties of the stego
document Intentional attacks on the other hand are deliberate attempts to distinguish
stego-objects from unmodified objects and thus detect the presence of covert
communication Attack methods generally exploit the fact that embedding information
usually changes the statistical properties of the objects compared to typical unmodified
objects In this proposed algorithm various attacks have been applied on the encrypted
image They are as follows
1)Shearing
2) Image Scaling
3) Image Rotating
4) Image color reduction
5) Image blurred
6) Image flip
7) cropping and intensity transformation
8) Image sharpening
9) Gaussian Noise and filtering
10) Image Contrast
11) Speckle Noise and Filtering
20
351 Shearing
The image is resized with the scale factor Resized image is rotated with
angle(theta)Finally spatial transformation from control point pairs is implemented
For example
Scale factor 09
Theta10
Fig 32 Shearing Image
352 Image Scaling
It resizes the image with a scale factor and rotation is performed It rotates the
image by angle (degrees) in a counterclockwise direction around its center point To
rotate the image clockwise specify a negative value for angle It makes the output image
large enough to contain the entire rotated image It uses nearest neighbour interpolation
setting the values of pixels in Output image that are outside the rotated image to 0 (zero)
For example
Scale Factor07
Theta30
scaling Image
21
Fig 33 Scaling Image
353 Rotation
It rotates the image by angle degrees in a counterclockwise direction around its
center point To rotate the image clockwise specify a negative value for angle It makes
the output image large enough to contain the entire rotated image It uses nearest
neighbour interpolation setting the values of pixels in Output image that are outside the
rotated image to 0 (zero)
For Example
Theta180
Fig 34 Rotation Image
354 Colour Reduced Image
It creates an indexed image approximation of the RGB image in the array RGB by
dithering the colors in the colormap map The colormap cannot have more than 65536
resized and rotated image
Rotated image
22
colors
For Example
Indexed image with 32 Colors
Fig 35 Colour Reduced Image
355 Blur Image
The image is blurred by using N-D filtering of multidimensional images It filters
the multidimensional array of original image with the multidimensional filter The array
of original image can be logical or a nonsparse numeric array of any class and dimension
The result image has the same size and class as of original image
Fig 36 Blur Image
Color reduced image
50 100 150 200 250 300 350 400 450 500
50
100
150
200
250
300
350
400
450
500
Blurred image
23
356 Flipped Image
It flips the image upside down Flipping is used to invert the image
Fig 37 Flipped Image
357 Cropped Image
It creates an interactive crop image tool associated with the image displayed in the
current figure called the target image The crop image tool is a movable resizable
rectangle that you can position interactively using the mouse When the crop image tool
is active the pointer changes to cross hairs when it is moved over the target image
Using the mouse image to be cropped can be specified by clicking and dragging the
mouse The crop rectangle using the mouse can be moved or resized When sizing and
positioning of the crop rectangle is finished create the cropped image by double-clicking
the left mouse button or by choosing crop image from the context menu Image cropping
returns the cropped image
Flipped image
24
Fig 38 Cropped Image
358 Intensity Transformation Adjust
It maps the intensity values in grayscale image to new values in resultant image
such that 1 of data is saturated at low and high intensity of original image This
increases the contrast of the output image
Fig 39 Intensity Transformation Image
Cropped Image
Intensity Transformation
25
359 Sharpening
Input array values outside the bounds of the array are assumed to equal the nearest
array border value The image is sharpened by using N-D filtering of multidimensional
images It filters the multidimensional array of original image with the multidimensional
filter The array of original image can be logical or a nonsparse numeric array of any
class and dimension The result image has the same size and class as of original image
Fig 310 Sharpened Image
3510 Gaussian Noise and Median Filtering
It adds Gaussian noise to the images Gaussian white noise have constant mean
and variance The noise added image is filtered using Median Filtering Median filtering
is a nonlinear operation often used in image processing to reduce salt and pepper noise
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Sharpened Image
26
Fig 311 Gaussian Noise and Median Filter Image
3511 Histogram of contrast image
It enhances the contrast of images by transforming the values in an intensity
image or the values in the colormap of an indexed image so that the histogram of the
output image approximately matches a specified histogram
Fig 312 Contrast Image
Gaussian Noise
Median Filtering
Contrast Image
27
Fig 313 Histogram of Contrast Image
3512 Speckle noise and Median Filtering
It adds multiplicative noise to the image I using the equation J = I+nI where n is
uniformly distributed random noise with mean 0 and variance v The default for v is 004
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Fig 314 Speckle Noise and Median Filter Image
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Histogram of Contrast Image
0 50 100 150 200 250
Speckle Noise
Median Filtering
28
36 Proposed Algorithm
Step1 Enter two keys a private key and a public key through which the RSA algorithm is
performed
Step2These key are used for encrypting the secret data using chaotic algorithm with
threshold of 2
Step3The secret data is converted into binary format from ASCII code
Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied
Step5The encryption is carried out with the key generated randomly in binary(1times256)
Step6 The 2D logistic substitution and permutation are carried out
Step7The resulting binary sequence is added with the encrypted text in LSB
Step8The image is converted to DNA sequence and transmitted
Step9 Various Attacks have been applied on the resultant image
Step10The inverse process is carried out to retrieve the original image and data
Step11The Performance Metrics have been calculated
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
2
1) The type of operations used for transforming Plaintext to Cipher text
All encryption algorithms are based on two general principles They are
substitution and transposition Substitution is the one in which each element in the plain
text is transformed into another element Transposition is the one in which elements in
the plain text are rearranged The fundamental condition is that no information be lost
2) The Number of keys used
Based on this we can classify the techniques into two
a) Symmetric-key Cryptography Symmetric-key cryptography refers to encryption
methods in which both the sender and receiver share the same key (or less commonly in
which their keys are different but related in an easily computable way)
Figure 11 Symmetric-key cryptography
b) Public key Cryptography In public-key cryptosystems the public key may be freely
distributed while its paired private key must remain secret In a public-key encryption
system the public key is used for encryption while the private or secret key is used for
decryption
Figure 12 Public key Cryptography
3
3) The way in which the plaintext is processed
There are 2 types
a) Block Cipher It processes the input one block of elements at a time producing an
output block for each input block
b) Stream Cipher It processes the input elements continuously producing output one
element at a time as it goes along
12 STEGANOGRAPHY
It is the art and science of encoding hidden messages in such a way that no one
apart from the sender and intended recipient suspects the existence of the message It is a
form of security through obscurity Generally the hidden messages will appear to be (or
be part of) something else images articles shopping lists or some other cover texts
Plainly visible encrypted messages no matter how unbreakable will arouse interest and
may in themselves be incriminating in countries where encryption is illegal For example
the hidden message may be in invisible ink between the visible lines of a private letter
The advantage of steganography over cryptography alone is that the intended secret
message does not attract attention to itself as an object of scrutiny So cryptography is the
practice of protecting the contents of a message alone steganography is concerned with
concealing the fact that a secret message is being sent as well as concealing the contents
of the message Steganography includes the concealment of information within computer
files In digital steganography electronic communications may include steganographic
coding inside of a transport layer such as a document file image file program or
protocol Media files are ideal for steganographic transmission because of their large size
There has been a rapid growth of interest in steganography for two main reasons
(i) The publishing and broadcasting industries have become interested in techniques for
hiding encrypted copyright marks and serial numbers in digital films audio
recordings books and multimedia products
(ii) Moves by various governments to restrict the availability of encryption services
have motivated people to study methods by which private messages can be
4
embedded in seemingly innocuous cover messages
Fig 13 Categories of Image Steganography
There are many applications for digital steganography of image including
copyright protection feature tagging and secret communication Copyright notice or
watermark can embedded inside an image to identify it as intellectual property If
someone attempts to use this image without permission we can prove by extracting the
watermark In feature tagging captions annotations time stamps and other descriptive
elements can be embedded inside an image Copying the stegondashimage also copies of the
embedded features and only parties who posses the decoding stego-key will be able to
extract and view the features On the other hand secret communication does not advertise
a covert communication by using steganography Therefore it can avoid scrutiny of the
sender message and recipient This is effective only if the hidden communication is not
detected by the others people In general steganography is two types reversible and
irreversible
5
13 Reversible Data Hiding
Figure 14 Reversible Data Hiding System
Secret Message The secret message or information to hide
Cover File Digital Medium The data or medium which concealed the secret message
Stego File A modified version of cover that contains the secret message
Key Additional secret data that is needed for the embedding and extracting processes
and must be known to both the sender and the recipient
Steganographic Method A steganographic function that takes cover secret message
and key as parameters and produces stego as output
Inverse of Steganographic Method A steganographic function that has stego and key
as parameters and produces secret message as output This is the inverse of method used
in embeding process in the sense that the result of the extracting process is identical to the
input of the embedding process
6
CHAPTER 2
LITERATURE SURVEY
1) Nidhi Sethi Deepika Sharma (2014) ldquoA Novel Method Of Image
Encryption using Logistic Mappingrdquo International Journal of Computer
Science Engineering (IJCSE)
This paper presents a new method to develop secure image-encryption techniques
using a logistics based encryption algorithm In this technique a Haar wavelet transform
was used to decompose the image and decorrelate its pixels into averaging and
differencing components The logistic based encryption algorithm produces a cipher of
the test image that has good diffusion and confusion properties The remaining
components (the differencing components) are compressed using a wavelet transform
Many test images are used to demonstrate the validity of the proposed algorithm The
results of several experiments show that the proposed algorithm for image cryptosystems
provides an efficient and secure approach to real-time image encryption and transmission
To send the keys in secure form steganography will be used Steganographic techniques
allow one party to communicate information to another party without a third party even
knowing that the communication is occurring
Advantages
(i) Efficient approach
(ii) Secure key transmission
(iii) Better image quality
7
2) Jing Wang Guoping Jiang (2013) ldquoA Self-Adaptive Parallel Encryption
Algorithm Based on Discrete 2D-Logistic Maprdquo International Journal of
Modern Nonlinear Theory and Application
A self-adaptive parallel encryption algorithm based on discrete 2D-Logistic
map is developed according to the position scrambling and diffusion of multi-direction in
variable space of spatial chaos The binary sequences b1b2b3bn are obtained according
to the user key in which the binary sequence 0 and 1 denote distribution mode of
processors and the number of binary sequence n denotes cycle number Then the
pseudorandom 2D matrix is generated by 2D-Logistic map and adaptive segmentation is
applied in image matrix and pseudorandom matrix according to the value and the number
of binary sequence The parallel operation is used among blocks to improve efficiency
and meet real-time demand in transmission processes However the pixel permutation is
applied in partitioned matrix through ergodic matrix generated by pseudo-random matrix-
block to decrease the correlation of adjacent pixels Then the pixel substitution is used for
fully diffusing through cipher block chaining mode until n cycles The proposed
algorithm can meet the three requirements of parallel operation in image encryption and
the real-time requirement in transmission processes The security is proved by theoretical
analysis and simulation results
Advantages
1Security is provided
2Effeciency is improved
8
3) LJani Anbarasi GSAnandha Mala Modigari Narendra (2014) ldquoDNA
based Multi-Secret Image Sharingrdquo International Conference on
Information and Communication Technologies (ICICT)
Multiple secret sharing algorithm using the YCH scheme combined with
DNA encoding is proposed focusing at better security Firstly DNA encoding for
multiple images is carried out then the addition of these encoded components by DNA is
performed Secondly the (t n) scheme used the Lagrange interpolation polynomial to
share these DNA scrambled matrices is performed bdquot‟ denotes threshold value and bdquon‟
denotes the number of participants The resulting scrambled images are encrypted into n
shares using Shamir‟s polynomial Thirdly these shares are embedded using a modular
operation Finally bdquot‟ or more shares are pooled which reconstructs the scrambled
matrices and by decoding the DNA scrambled matrices multiple secrets are
reconstructed without loss The simulation results and the security analysis prove that this
algorithm is perfect and produces results with better PSNR value The correlation co-
efficient shows that this also has the ability of resisting various attacks
Advantages
1Security is better
2Resistance against Attack
9
4)Della Babya Jitha Thomasa Gisny Augustinea Elsa Georgea Neenu
Rosia Michaela (2014) ldquoA Novel DWT based Image Securing Method using
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Steganography is a data hiding technique that is widely used in various
information securing applications Steganography transmits data by hiding the existence
of the message so that a viewer cannot identify the transmission of message and hence
not able to decrypt it This work proposes a data securing technique that is used for
hiding multiple color images into a single color image using the Discrete Wavelet
Transform The cover image is split up into R G and B planes Secret images are
embedded into these planes An N-level decomposition of the cover image and the secret
images are done and some frequency components of the same are combined Secret
images are then extracted from the stego image Here the stego image obtained has a less
perceptible changes compared to the original image with high overall security
Advantages
1Less perceptible changes
2Overall security is high
10
5)Prasenjit Dasa Subhrajyoti Deba Nirmalya Kara Baby
Bhattacharyaa(2014)ldquoAn Improved DNA based Dual Cover
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Dual cover steganography is an evolving technique in the field of covert
data transmission This paper focuses on the concept of using a theoretical single stranded
DNA (ssDNA) as a primary cover which is extracted from an inconspicuous cover
image They have analyzed the security loopholes and performance issues of the existing
algorithm and proposed an improved algorithm on the same basisthey use 2D Logistic
map for encrypting the cover imageThen overall encryption is RC43 types of encryption
is generally used Performance of both the algorithms are tested against several visual
and statistical attacks and parameterized in terms of both security and capacity The
comparison shows that the proposed improvements provide better overall security
Advantages
1 Robustness against various attack
2 Performance measure are calculated
3 Data hiding improves security
11
6) Bhavana Agrawal Himani Agrawal ( May-2013) ldquoImplementation of AES
and RSA Using Chaos systemrdquo International Journal of Scientific amp
Engineering Research Vol 4 No 5
This paper presents two cryptographic algorithm AES and RSA Using Chaos
Chaos has attracted much attention in the field of cryptography It describes a system
which is sensitive to initial condition It generates apparently random behavior but at the
same time is completely deterministic Chaos function is used to increase the complexity
and Security of the SystemAES and RSA are the two cryptographic algorithms In AES
we apply the Chaos on S-box where as in RSA we mix the plaintext with Chaos sequence
First then apply for encryption and decryption After Implementing AES and RSA they
compare both the technique on the basis of speed
Advantages
1Chaos function is used to improve complexity
2The speed has been improved with combined technique of AES and RSA along with
chaos technique
12
7) Pahrul Irfan Yudi Prayudi Imam Riadi ( August 2015) ldquo Image
Encryption using Combination of Chaotic System and Rivers Shamir
Adleman (RSA)rdquo International Journal of Computer Applications Vol 123
No6
Security and confidentiality of data or information at the present time has
become an important concern Advanced methods for secure transmission storage and
retrieval of digital images are increasingly needed for a number of military medical
homeland security and other applications Various kinds of techniques for increase
security data or information already is developed one common way is by cryptographic
techniques Cryptography is science to maintain the security of the message by changing
data or information into a different form so the message cannot be recognized To
compensate for increasing computing speeds increases it takes more than one encryption
algorithm to improve security of digital images One way is by using algorithms to
double cryptography do encryption and decryption Cryptographic algorithm often used
today and the proven strength specially the digital image is Algorithm with Chaos
system To improve security at the image then we use Additional algorithms namely
Rivers algorithm Shamir Adleman (RSA) which known as the standard of cryptography
algorithms This research aims to optimize security bitmap image format by combining
the two algorithms namely Chaos-based algorithms and RSA algorithm into one
application Experiments conducted show that the proposed algorithm possesses robust
security features such as fairly uniform distribution high sensitivity to both keys and
plain images almost ideal entropy and the ability to highly de-correlate adjacent pixels
in the cipher images Furthermore it has a large key space and transform image to pure
text file which greatly increases its security for image encryption
Advantages
1 It aims to optimize security bitmap image format by combining the two algorithms
namely Chaos-based algorithms and RSA algorithm into one application
13
8) Weiming Zhang KedeMa NenghaiYu(2013) ldquo Reversibility improved
data hiding in encrypted imagesrdquo School of Information Science and
Technology
A novel reversible data hiding technique in encrypted images is presented in this
paper Instead of embedding data in encrypted images directly some pixels are estimated
before encryption so that additional data can be embedded in the estimating errors A
bench mark encryption algorithm (eg AES) is applied to the rest pixels of the image and
a special encryption scheme is designed to encrypt the estimating errors Without the
encryption key one cannot get access to the original image However provided with the
data hiding key only he can embed in or extract from the encrypted image additional data
without knowledge about the original image Moreover the data extraction and image
recovery are free of errors for all images Experiments demonstrate the feasibility and
efficiency of the proposed method especially in aspect of embedding rate versus Peak
Signal-to-Noise Ratio (PSNR)
The paper proposes a novel method to significantly improve the performance by
reversing the order of encryption and vacating room In the light of this idea we empty
out room prior to image encryption by shifting the histogram of estimating errors of some
pixels and the emptied out room will be used for data hiding The proposed method is
composed of four primary steps vacating room and encrypting image data hiding in the
encrypted image data extraction and image recovery Two different schemes extraction
before decryption and decryption before extraction are raised to cope with different
applications
Advantages
(i) Achieves excellent performance in three aspects complete reversibility PSNR
under given embedding rate separability between data higher extraction and
image decryption
14
CHAPTER 3
PROPOSED METHODOLOGY
The proposed data hiding scheme aims at the security of the hidden data
Embedding is performed in spatial domain The data to be embedded is converted into
binary form from ASCII code using chaos encryption and is embedded into the cover
image obtained after 2D logistic map This embedded image is secured using symmetric
key (K1)They are converted into DNA sequence to provide additional level of security
The hidden data can be extracted from the cover image accurately with the help of
decryption techniques and secret key (K1) The cover image can also be extracted
without any distortion The fig 31 shows the workflow
Fig 31 Work Flow Diagram
SECRET DATA
COVER IMAGE
CHAOTIC
ENCRYPTION
ENCRY 2D LOGISTIC
ENCRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
CHAOTIC
DECRYPTION
ENCRY
SECRET DATA
COVER IMAGE 2D LOGISTIC
DECRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
15
31 Chaotic Encryption
Chaotic cryptography is the application of the mathematical chaos theory to the
practice of the cryptography the study or techniques used to privately and securely
transmit information with the presence of an third-party or adversary The use of chaos
or randomness in cryptography has long been sought after by entities wanting a new way
to encrypt messages However because of the lack of thorough provable security
properties and low acceptable performance chaotic cryptography has encountered
setbacksIn order to use chaos theory acceptably in cryptography they must first be
mapped to each other Properties in chaotic systems and cryptographic primitives share
unique characteristics that allow for the chaotic systems to be applied to cryptography If
chaotic parameters as well as cryptographic keys can be mapped symmetrically or
mapped to produce acceptable and functional outputs it will make it next to impossible
for an adversary to find the outputs without any knowledge the initial values Since
chaotic maps in a real life scenario require a set of numbers that are limited they may in
fact have no real purpose in a cryptosystem if the chaotic behavior can be predicted To
counter this possibility there exists simple to advanced ciphers Chaos theory used in
cryptosystems for commercial implementation has proven to be unsuccessful mainly
because a chaos theories‟ requirement to use intervals of real numbers Given enough
resources and time an adversary could be able to predict functional outcomes Since
chaotic cryptosystems have no root in number theory this would make it difficult or
impossible to implement therefore impractical
32 The RSA Algorithm
The RSA cryptosystem named after its inventors R Rivest A Shamir and L
Adleman is the most widely used public key Cryptosystem It may be used to provide
both secrecy and digital signatures and its security is based on the intractability of the
integer factorizationThe RSA algorithm involves three steps key generation encryption
and decryption
16
321 Key Generation
RSA involves a public key and a private key The public key can be known to
everyone and is used for encrypting messages Messages encrypted with the public key
can only be decrypted in a reasonable amount of time using the private key The keys for
the RSA algorithm are generated the following way To generate the two keys choose
two random large prime numbers p and q For maximum security choose p and q of
equal length Then randomly choose the encryption key e such that e and ( p minus1) (q minus1)
are relatively prime Finally use the extended Euclidean algorithm to compute the
decryption key d such that
d= e-1
mod ( (p-1) (q-1))
Note that d and n are also relatively prime The numbers e and K are the public
key the number d is the private key The two primes p and q are no longer needed They
should be discarded but never revealed
322 Encryption
Firstly receiver transmits her public key (n e) to sender and keeps the private key
secret If sender wishes to send message M to receiver Sender change the message M in
to integer m such that 0 le mltn Then sender computes the cipher text c corresponding to
Cequiv me
(mod n)
323 Decryption
Receiver can recover M from c by using private key exponent d via computing
M equiv cd
(mod n)
17
Algorithm
1Select any two prime numbers say (pq)
2Compute n=pq and also compute empty(119899)=(p-1)(q-1)
3Choose e such that 1ltelt empty(119899)
4Choose d such that (de)mod empty(119899)=1
5Public key is (en) and Private key is (dn)
6 If egt=2 then check i==1 if so return 1 else return 0
7In a iteration check for e(i)==1 if so take mod function of message with n
8Message is converted to cipher text in ASCII form with the key generated
9The cipher data in ASCII form is converted to binary form
33 2D Logistic Encryption
The chaotic system is a deterministic nonlinear system It possesses a varied
characteristics such as high sensitivity to initial conditions and system parameters
random-like behaviors and so forth Chaotic sequences produced by chaotic maps are
pseudo-random sequences their structures are very complex and difficult to be analyzed
and predicted In other words chaotic systems can improve the security of encryption
systems Thus it is advisable to encrypt digital image with chaotic systems There are
two chaotic maps one is logistic map and the other is 2D logistic map In the proposed
work 2D logistic map is used
Logistic map is an example for chaotic map and it is described as follows
x(n+1)=μ x(n)(1minusx(n))
μ is a positive constant sometimes known as the biotic potential gives the so-called
logistic map x(n) is series of values used to plot Logistic mapwhere μ isin [04] x(n) isin
(01) and n = 01 2hellip The research result shows that the system is in chaotic state
under the condition that 356994 lt μ le 4
18
2D logistic map is described in as follows
119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2
119910119894+1 = 1205832 lowast 119910119894(1 minus 119910119894) + 1205732((119909119894)2 + 119909119894 lowast 119910119894)
Where z(xy) is the logistic map 1205831 and 1205832 are positive constant sometimes known as the
biotic potential gives the so-called logistic map x be the position of images in x axis
and y be the position of images in y axis 1205731 and 1205732 are the correlation constantsWhen
275 lt μ1 le 34 275 lt μ2 le 345 015 lt 1205731le 021 and 013 lt 1205732 le 015 the system is in
chaotic state and can generate two chaotic sequences in the region (01] Due to the
system parameter γ1 and γ2 which have smaller value range we set γ1 = 017 and γ2 =
014 other parameters can be seen as secret keys
Algorithm
1A random key is generated in binary form ( 1times256) and it is stored in a array
2The random key generated is translated to map format using block processing (4times4)
3 The row and column wise transformation is carried out
4The key is now used to encrypt the cover image
52D logistic image undergoes substitution and permutation (column and row wise
shuffling is done)
34 DNA Sequence
A single DNA sequence is made up of four nucleic acid
bases A (adenine) C (cytosine) G (guanine) and T (thymine) where A and T are
complements and C and G are complements Let binary number 0 and 1 be
complements so 00 and 11 are complements and 01 and 10 are complements Thus we
can use these four bases A T G and C to encode 01 10 00 and 11 respectively The
encoding method still satisfies the Watson-Crick complement rule Usually each pixel
value of the 8 bit grey image can be expressed to 8 bits binary stream The binary stream
can be encoded to a DNA sequence whose length is 4 For example if the first pixel
19
value of the original image is 75 convert it into a binary stream [01001011] By using the
above DNA encoding rule to encode the stream we can get a DNA sequence [AGTC]
whereas we use A T G and C to express 01 10 00 and 11 respectively We can get a
binary sequence [01001011]
35 Attacks
The steganographic algorithm is used to embed secret messages into cover
image To obtain stego image while exchanging these stego-image through the public
communication channel various attacks have been made The are generally classified
into two types intentional or unintentional attacks Examples of unintentional attacks are
transmission errors lossy compression and changing the visual properties of the stego
document Intentional attacks on the other hand are deliberate attempts to distinguish
stego-objects from unmodified objects and thus detect the presence of covert
communication Attack methods generally exploit the fact that embedding information
usually changes the statistical properties of the objects compared to typical unmodified
objects In this proposed algorithm various attacks have been applied on the encrypted
image They are as follows
1)Shearing
2) Image Scaling
3) Image Rotating
4) Image color reduction
5) Image blurred
6) Image flip
7) cropping and intensity transformation
8) Image sharpening
9) Gaussian Noise and filtering
10) Image Contrast
11) Speckle Noise and Filtering
20
351 Shearing
The image is resized with the scale factor Resized image is rotated with
angle(theta)Finally spatial transformation from control point pairs is implemented
For example
Scale factor 09
Theta10
Fig 32 Shearing Image
352 Image Scaling
It resizes the image with a scale factor and rotation is performed It rotates the
image by angle (degrees) in a counterclockwise direction around its center point To
rotate the image clockwise specify a negative value for angle It makes the output image
large enough to contain the entire rotated image It uses nearest neighbour interpolation
setting the values of pixels in Output image that are outside the rotated image to 0 (zero)
For example
Scale Factor07
Theta30
scaling Image
21
Fig 33 Scaling Image
353 Rotation
It rotates the image by angle degrees in a counterclockwise direction around its
center point To rotate the image clockwise specify a negative value for angle It makes
the output image large enough to contain the entire rotated image It uses nearest
neighbour interpolation setting the values of pixels in Output image that are outside the
rotated image to 0 (zero)
For Example
Theta180
Fig 34 Rotation Image
354 Colour Reduced Image
It creates an indexed image approximation of the RGB image in the array RGB by
dithering the colors in the colormap map The colormap cannot have more than 65536
resized and rotated image
Rotated image
22
colors
For Example
Indexed image with 32 Colors
Fig 35 Colour Reduced Image
355 Blur Image
The image is blurred by using N-D filtering of multidimensional images It filters
the multidimensional array of original image with the multidimensional filter The array
of original image can be logical or a nonsparse numeric array of any class and dimension
The result image has the same size and class as of original image
Fig 36 Blur Image
Color reduced image
50 100 150 200 250 300 350 400 450 500
50
100
150
200
250
300
350
400
450
500
Blurred image
23
356 Flipped Image
It flips the image upside down Flipping is used to invert the image
Fig 37 Flipped Image
357 Cropped Image
It creates an interactive crop image tool associated with the image displayed in the
current figure called the target image The crop image tool is a movable resizable
rectangle that you can position interactively using the mouse When the crop image tool
is active the pointer changes to cross hairs when it is moved over the target image
Using the mouse image to be cropped can be specified by clicking and dragging the
mouse The crop rectangle using the mouse can be moved or resized When sizing and
positioning of the crop rectangle is finished create the cropped image by double-clicking
the left mouse button or by choosing crop image from the context menu Image cropping
returns the cropped image
Flipped image
24
Fig 38 Cropped Image
358 Intensity Transformation Adjust
It maps the intensity values in grayscale image to new values in resultant image
such that 1 of data is saturated at low and high intensity of original image This
increases the contrast of the output image
Fig 39 Intensity Transformation Image
Cropped Image
Intensity Transformation
25
359 Sharpening
Input array values outside the bounds of the array are assumed to equal the nearest
array border value The image is sharpened by using N-D filtering of multidimensional
images It filters the multidimensional array of original image with the multidimensional
filter The array of original image can be logical or a nonsparse numeric array of any
class and dimension The result image has the same size and class as of original image
Fig 310 Sharpened Image
3510 Gaussian Noise and Median Filtering
It adds Gaussian noise to the images Gaussian white noise have constant mean
and variance The noise added image is filtered using Median Filtering Median filtering
is a nonlinear operation often used in image processing to reduce salt and pepper noise
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Sharpened Image
26
Fig 311 Gaussian Noise and Median Filter Image
3511 Histogram of contrast image
It enhances the contrast of images by transforming the values in an intensity
image or the values in the colormap of an indexed image so that the histogram of the
output image approximately matches a specified histogram
Fig 312 Contrast Image
Gaussian Noise
Median Filtering
Contrast Image
27
Fig 313 Histogram of Contrast Image
3512 Speckle noise and Median Filtering
It adds multiplicative noise to the image I using the equation J = I+nI where n is
uniformly distributed random noise with mean 0 and variance v The default for v is 004
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Fig 314 Speckle Noise and Median Filter Image
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Histogram of Contrast Image
0 50 100 150 200 250
Speckle Noise
Median Filtering
28
36 Proposed Algorithm
Step1 Enter two keys a private key and a public key through which the RSA algorithm is
performed
Step2These key are used for encrypting the secret data using chaotic algorithm with
threshold of 2
Step3The secret data is converted into binary format from ASCII code
Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied
Step5The encryption is carried out with the key generated randomly in binary(1times256)
Step6 The 2D logistic substitution and permutation are carried out
Step7The resulting binary sequence is added with the encrypted text in LSB
Step8The image is converted to DNA sequence and transmitted
Step9 Various Attacks have been applied on the resultant image
Step10The inverse process is carried out to retrieve the original image and data
Step11The Performance Metrics have been calculated
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
3
3) The way in which the plaintext is processed
There are 2 types
a) Block Cipher It processes the input one block of elements at a time producing an
output block for each input block
b) Stream Cipher It processes the input elements continuously producing output one
element at a time as it goes along
12 STEGANOGRAPHY
It is the art and science of encoding hidden messages in such a way that no one
apart from the sender and intended recipient suspects the existence of the message It is a
form of security through obscurity Generally the hidden messages will appear to be (or
be part of) something else images articles shopping lists or some other cover texts
Plainly visible encrypted messages no matter how unbreakable will arouse interest and
may in themselves be incriminating in countries where encryption is illegal For example
the hidden message may be in invisible ink between the visible lines of a private letter
The advantage of steganography over cryptography alone is that the intended secret
message does not attract attention to itself as an object of scrutiny So cryptography is the
practice of protecting the contents of a message alone steganography is concerned with
concealing the fact that a secret message is being sent as well as concealing the contents
of the message Steganography includes the concealment of information within computer
files In digital steganography electronic communications may include steganographic
coding inside of a transport layer such as a document file image file program or
protocol Media files are ideal for steganographic transmission because of their large size
There has been a rapid growth of interest in steganography for two main reasons
(i) The publishing and broadcasting industries have become interested in techniques for
hiding encrypted copyright marks and serial numbers in digital films audio
recordings books and multimedia products
(ii) Moves by various governments to restrict the availability of encryption services
have motivated people to study methods by which private messages can be
4
embedded in seemingly innocuous cover messages
Fig 13 Categories of Image Steganography
There are many applications for digital steganography of image including
copyright protection feature tagging and secret communication Copyright notice or
watermark can embedded inside an image to identify it as intellectual property If
someone attempts to use this image without permission we can prove by extracting the
watermark In feature tagging captions annotations time stamps and other descriptive
elements can be embedded inside an image Copying the stegondashimage also copies of the
embedded features and only parties who posses the decoding stego-key will be able to
extract and view the features On the other hand secret communication does not advertise
a covert communication by using steganography Therefore it can avoid scrutiny of the
sender message and recipient This is effective only if the hidden communication is not
detected by the others people In general steganography is two types reversible and
irreversible
5
13 Reversible Data Hiding
Figure 14 Reversible Data Hiding System
Secret Message The secret message or information to hide
Cover File Digital Medium The data or medium which concealed the secret message
Stego File A modified version of cover that contains the secret message
Key Additional secret data that is needed for the embedding and extracting processes
and must be known to both the sender and the recipient
Steganographic Method A steganographic function that takes cover secret message
and key as parameters and produces stego as output
Inverse of Steganographic Method A steganographic function that has stego and key
as parameters and produces secret message as output This is the inverse of method used
in embeding process in the sense that the result of the extracting process is identical to the
input of the embedding process
6
CHAPTER 2
LITERATURE SURVEY
1) Nidhi Sethi Deepika Sharma (2014) ldquoA Novel Method Of Image
Encryption using Logistic Mappingrdquo International Journal of Computer
Science Engineering (IJCSE)
This paper presents a new method to develop secure image-encryption techniques
using a logistics based encryption algorithm In this technique a Haar wavelet transform
was used to decompose the image and decorrelate its pixels into averaging and
differencing components The logistic based encryption algorithm produces a cipher of
the test image that has good diffusion and confusion properties The remaining
components (the differencing components) are compressed using a wavelet transform
Many test images are used to demonstrate the validity of the proposed algorithm The
results of several experiments show that the proposed algorithm for image cryptosystems
provides an efficient and secure approach to real-time image encryption and transmission
To send the keys in secure form steganography will be used Steganographic techniques
allow one party to communicate information to another party without a third party even
knowing that the communication is occurring
Advantages
(i) Efficient approach
(ii) Secure key transmission
(iii) Better image quality
7
2) Jing Wang Guoping Jiang (2013) ldquoA Self-Adaptive Parallel Encryption
Algorithm Based on Discrete 2D-Logistic Maprdquo International Journal of
Modern Nonlinear Theory and Application
A self-adaptive parallel encryption algorithm based on discrete 2D-Logistic
map is developed according to the position scrambling and diffusion of multi-direction in
variable space of spatial chaos The binary sequences b1b2b3bn are obtained according
to the user key in which the binary sequence 0 and 1 denote distribution mode of
processors and the number of binary sequence n denotes cycle number Then the
pseudorandom 2D matrix is generated by 2D-Logistic map and adaptive segmentation is
applied in image matrix and pseudorandom matrix according to the value and the number
of binary sequence The parallel operation is used among blocks to improve efficiency
and meet real-time demand in transmission processes However the pixel permutation is
applied in partitioned matrix through ergodic matrix generated by pseudo-random matrix-
block to decrease the correlation of adjacent pixels Then the pixel substitution is used for
fully diffusing through cipher block chaining mode until n cycles The proposed
algorithm can meet the three requirements of parallel operation in image encryption and
the real-time requirement in transmission processes The security is proved by theoretical
analysis and simulation results
Advantages
1Security is provided
2Effeciency is improved
8
3) LJani Anbarasi GSAnandha Mala Modigari Narendra (2014) ldquoDNA
based Multi-Secret Image Sharingrdquo International Conference on
Information and Communication Technologies (ICICT)
Multiple secret sharing algorithm using the YCH scheme combined with
DNA encoding is proposed focusing at better security Firstly DNA encoding for
multiple images is carried out then the addition of these encoded components by DNA is
performed Secondly the (t n) scheme used the Lagrange interpolation polynomial to
share these DNA scrambled matrices is performed bdquot‟ denotes threshold value and bdquon‟
denotes the number of participants The resulting scrambled images are encrypted into n
shares using Shamir‟s polynomial Thirdly these shares are embedded using a modular
operation Finally bdquot‟ or more shares are pooled which reconstructs the scrambled
matrices and by decoding the DNA scrambled matrices multiple secrets are
reconstructed without loss The simulation results and the security analysis prove that this
algorithm is perfect and produces results with better PSNR value The correlation co-
efficient shows that this also has the ability of resisting various attacks
Advantages
1Security is better
2Resistance against Attack
9
4)Della Babya Jitha Thomasa Gisny Augustinea Elsa Georgea Neenu
Rosia Michaela (2014) ldquoA Novel DWT based Image Securing Method using
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Steganography is a data hiding technique that is widely used in various
information securing applications Steganography transmits data by hiding the existence
of the message so that a viewer cannot identify the transmission of message and hence
not able to decrypt it This work proposes a data securing technique that is used for
hiding multiple color images into a single color image using the Discrete Wavelet
Transform The cover image is split up into R G and B planes Secret images are
embedded into these planes An N-level decomposition of the cover image and the secret
images are done and some frequency components of the same are combined Secret
images are then extracted from the stego image Here the stego image obtained has a less
perceptible changes compared to the original image with high overall security
Advantages
1Less perceptible changes
2Overall security is high
10
5)Prasenjit Dasa Subhrajyoti Deba Nirmalya Kara Baby
Bhattacharyaa(2014)ldquoAn Improved DNA based Dual Cover
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Dual cover steganography is an evolving technique in the field of covert
data transmission This paper focuses on the concept of using a theoretical single stranded
DNA (ssDNA) as a primary cover which is extracted from an inconspicuous cover
image They have analyzed the security loopholes and performance issues of the existing
algorithm and proposed an improved algorithm on the same basisthey use 2D Logistic
map for encrypting the cover imageThen overall encryption is RC43 types of encryption
is generally used Performance of both the algorithms are tested against several visual
and statistical attacks and parameterized in terms of both security and capacity The
comparison shows that the proposed improvements provide better overall security
Advantages
1 Robustness against various attack
2 Performance measure are calculated
3 Data hiding improves security
11
6) Bhavana Agrawal Himani Agrawal ( May-2013) ldquoImplementation of AES
and RSA Using Chaos systemrdquo International Journal of Scientific amp
Engineering Research Vol 4 No 5
This paper presents two cryptographic algorithm AES and RSA Using Chaos
Chaos has attracted much attention in the field of cryptography It describes a system
which is sensitive to initial condition It generates apparently random behavior but at the
same time is completely deterministic Chaos function is used to increase the complexity
and Security of the SystemAES and RSA are the two cryptographic algorithms In AES
we apply the Chaos on S-box where as in RSA we mix the plaintext with Chaos sequence
First then apply for encryption and decryption After Implementing AES and RSA they
compare both the technique on the basis of speed
Advantages
1Chaos function is used to improve complexity
2The speed has been improved with combined technique of AES and RSA along with
chaos technique
12
7) Pahrul Irfan Yudi Prayudi Imam Riadi ( August 2015) ldquo Image
Encryption using Combination of Chaotic System and Rivers Shamir
Adleman (RSA)rdquo International Journal of Computer Applications Vol 123
No6
Security and confidentiality of data or information at the present time has
become an important concern Advanced methods for secure transmission storage and
retrieval of digital images are increasingly needed for a number of military medical
homeland security and other applications Various kinds of techniques for increase
security data or information already is developed one common way is by cryptographic
techniques Cryptography is science to maintain the security of the message by changing
data or information into a different form so the message cannot be recognized To
compensate for increasing computing speeds increases it takes more than one encryption
algorithm to improve security of digital images One way is by using algorithms to
double cryptography do encryption and decryption Cryptographic algorithm often used
today and the proven strength specially the digital image is Algorithm with Chaos
system To improve security at the image then we use Additional algorithms namely
Rivers algorithm Shamir Adleman (RSA) which known as the standard of cryptography
algorithms This research aims to optimize security bitmap image format by combining
the two algorithms namely Chaos-based algorithms and RSA algorithm into one
application Experiments conducted show that the proposed algorithm possesses robust
security features such as fairly uniform distribution high sensitivity to both keys and
plain images almost ideal entropy and the ability to highly de-correlate adjacent pixels
in the cipher images Furthermore it has a large key space and transform image to pure
text file which greatly increases its security for image encryption
Advantages
1 It aims to optimize security bitmap image format by combining the two algorithms
namely Chaos-based algorithms and RSA algorithm into one application
13
8) Weiming Zhang KedeMa NenghaiYu(2013) ldquo Reversibility improved
data hiding in encrypted imagesrdquo School of Information Science and
Technology
A novel reversible data hiding technique in encrypted images is presented in this
paper Instead of embedding data in encrypted images directly some pixels are estimated
before encryption so that additional data can be embedded in the estimating errors A
bench mark encryption algorithm (eg AES) is applied to the rest pixels of the image and
a special encryption scheme is designed to encrypt the estimating errors Without the
encryption key one cannot get access to the original image However provided with the
data hiding key only he can embed in or extract from the encrypted image additional data
without knowledge about the original image Moreover the data extraction and image
recovery are free of errors for all images Experiments demonstrate the feasibility and
efficiency of the proposed method especially in aspect of embedding rate versus Peak
Signal-to-Noise Ratio (PSNR)
The paper proposes a novel method to significantly improve the performance by
reversing the order of encryption and vacating room In the light of this idea we empty
out room prior to image encryption by shifting the histogram of estimating errors of some
pixels and the emptied out room will be used for data hiding The proposed method is
composed of four primary steps vacating room and encrypting image data hiding in the
encrypted image data extraction and image recovery Two different schemes extraction
before decryption and decryption before extraction are raised to cope with different
applications
Advantages
(i) Achieves excellent performance in three aspects complete reversibility PSNR
under given embedding rate separability between data higher extraction and
image decryption
14
CHAPTER 3
PROPOSED METHODOLOGY
The proposed data hiding scheme aims at the security of the hidden data
Embedding is performed in spatial domain The data to be embedded is converted into
binary form from ASCII code using chaos encryption and is embedded into the cover
image obtained after 2D logistic map This embedded image is secured using symmetric
key (K1)They are converted into DNA sequence to provide additional level of security
The hidden data can be extracted from the cover image accurately with the help of
decryption techniques and secret key (K1) The cover image can also be extracted
without any distortion The fig 31 shows the workflow
Fig 31 Work Flow Diagram
SECRET DATA
COVER IMAGE
CHAOTIC
ENCRYPTION
ENCRY 2D LOGISTIC
ENCRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
CHAOTIC
DECRYPTION
ENCRY
SECRET DATA
COVER IMAGE 2D LOGISTIC
DECRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
15
31 Chaotic Encryption
Chaotic cryptography is the application of the mathematical chaos theory to the
practice of the cryptography the study or techniques used to privately and securely
transmit information with the presence of an third-party or adversary The use of chaos
or randomness in cryptography has long been sought after by entities wanting a new way
to encrypt messages However because of the lack of thorough provable security
properties and low acceptable performance chaotic cryptography has encountered
setbacksIn order to use chaos theory acceptably in cryptography they must first be
mapped to each other Properties in chaotic systems and cryptographic primitives share
unique characteristics that allow for the chaotic systems to be applied to cryptography If
chaotic parameters as well as cryptographic keys can be mapped symmetrically or
mapped to produce acceptable and functional outputs it will make it next to impossible
for an adversary to find the outputs without any knowledge the initial values Since
chaotic maps in a real life scenario require a set of numbers that are limited they may in
fact have no real purpose in a cryptosystem if the chaotic behavior can be predicted To
counter this possibility there exists simple to advanced ciphers Chaos theory used in
cryptosystems for commercial implementation has proven to be unsuccessful mainly
because a chaos theories‟ requirement to use intervals of real numbers Given enough
resources and time an adversary could be able to predict functional outcomes Since
chaotic cryptosystems have no root in number theory this would make it difficult or
impossible to implement therefore impractical
32 The RSA Algorithm
The RSA cryptosystem named after its inventors R Rivest A Shamir and L
Adleman is the most widely used public key Cryptosystem It may be used to provide
both secrecy and digital signatures and its security is based on the intractability of the
integer factorizationThe RSA algorithm involves three steps key generation encryption
and decryption
16
321 Key Generation
RSA involves a public key and a private key The public key can be known to
everyone and is used for encrypting messages Messages encrypted with the public key
can only be decrypted in a reasonable amount of time using the private key The keys for
the RSA algorithm are generated the following way To generate the two keys choose
two random large prime numbers p and q For maximum security choose p and q of
equal length Then randomly choose the encryption key e such that e and ( p minus1) (q minus1)
are relatively prime Finally use the extended Euclidean algorithm to compute the
decryption key d such that
d= e-1
mod ( (p-1) (q-1))
Note that d and n are also relatively prime The numbers e and K are the public
key the number d is the private key The two primes p and q are no longer needed They
should be discarded but never revealed
322 Encryption
Firstly receiver transmits her public key (n e) to sender and keeps the private key
secret If sender wishes to send message M to receiver Sender change the message M in
to integer m such that 0 le mltn Then sender computes the cipher text c corresponding to
Cequiv me
(mod n)
323 Decryption
Receiver can recover M from c by using private key exponent d via computing
M equiv cd
(mod n)
17
Algorithm
1Select any two prime numbers say (pq)
2Compute n=pq and also compute empty(119899)=(p-1)(q-1)
3Choose e such that 1ltelt empty(119899)
4Choose d such that (de)mod empty(119899)=1
5Public key is (en) and Private key is (dn)
6 If egt=2 then check i==1 if so return 1 else return 0
7In a iteration check for e(i)==1 if so take mod function of message with n
8Message is converted to cipher text in ASCII form with the key generated
9The cipher data in ASCII form is converted to binary form
33 2D Logistic Encryption
The chaotic system is a deterministic nonlinear system It possesses a varied
characteristics such as high sensitivity to initial conditions and system parameters
random-like behaviors and so forth Chaotic sequences produced by chaotic maps are
pseudo-random sequences their structures are very complex and difficult to be analyzed
and predicted In other words chaotic systems can improve the security of encryption
systems Thus it is advisable to encrypt digital image with chaotic systems There are
two chaotic maps one is logistic map and the other is 2D logistic map In the proposed
work 2D logistic map is used
Logistic map is an example for chaotic map and it is described as follows
x(n+1)=μ x(n)(1minusx(n))
μ is a positive constant sometimes known as the biotic potential gives the so-called
logistic map x(n) is series of values used to plot Logistic mapwhere μ isin [04] x(n) isin
(01) and n = 01 2hellip The research result shows that the system is in chaotic state
under the condition that 356994 lt μ le 4
18
2D logistic map is described in as follows
119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2
119910119894+1 = 1205832 lowast 119910119894(1 minus 119910119894) + 1205732((119909119894)2 + 119909119894 lowast 119910119894)
Where z(xy) is the logistic map 1205831 and 1205832 are positive constant sometimes known as the
biotic potential gives the so-called logistic map x be the position of images in x axis
and y be the position of images in y axis 1205731 and 1205732 are the correlation constantsWhen
275 lt μ1 le 34 275 lt μ2 le 345 015 lt 1205731le 021 and 013 lt 1205732 le 015 the system is in
chaotic state and can generate two chaotic sequences in the region (01] Due to the
system parameter γ1 and γ2 which have smaller value range we set γ1 = 017 and γ2 =
014 other parameters can be seen as secret keys
Algorithm
1A random key is generated in binary form ( 1times256) and it is stored in a array
2The random key generated is translated to map format using block processing (4times4)
3 The row and column wise transformation is carried out
4The key is now used to encrypt the cover image
52D logistic image undergoes substitution and permutation (column and row wise
shuffling is done)
34 DNA Sequence
A single DNA sequence is made up of four nucleic acid
bases A (adenine) C (cytosine) G (guanine) and T (thymine) where A and T are
complements and C and G are complements Let binary number 0 and 1 be
complements so 00 and 11 are complements and 01 and 10 are complements Thus we
can use these four bases A T G and C to encode 01 10 00 and 11 respectively The
encoding method still satisfies the Watson-Crick complement rule Usually each pixel
value of the 8 bit grey image can be expressed to 8 bits binary stream The binary stream
can be encoded to a DNA sequence whose length is 4 For example if the first pixel
19
value of the original image is 75 convert it into a binary stream [01001011] By using the
above DNA encoding rule to encode the stream we can get a DNA sequence [AGTC]
whereas we use A T G and C to express 01 10 00 and 11 respectively We can get a
binary sequence [01001011]
35 Attacks
The steganographic algorithm is used to embed secret messages into cover
image To obtain stego image while exchanging these stego-image through the public
communication channel various attacks have been made The are generally classified
into two types intentional or unintentional attacks Examples of unintentional attacks are
transmission errors lossy compression and changing the visual properties of the stego
document Intentional attacks on the other hand are deliberate attempts to distinguish
stego-objects from unmodified objects and thus detect the presence of covert
communication Attack methods generally exploit the fact that embedding information
usually changes the statistical properties of the objects compared to typical unmodified
objects In this proposed algorithm various attacks have been applied on the encrypted
image They are as follows
1)Shearing
2) Image Scaling
3) Image Rotating
4) Image color reduction
5) Image blurred
6) Image flip
7) cropping and intensity transformation
8) Image sharpening
9) Gaussian Noise and filtering
10) Image Contrast
11) Speckle Noise and Filtering
20
351 Shearing
The image is resized with the scale factor Resized image is rotated with
angle(theta)Finally spatial transformation from control point pairs is implemented
For example
Scale factor 09
Theta10
Fig 32 Shearing Image
352 Image Scaling
It resizes the image with a scale factor and rotation is performed It rotates the
image by angle (degrees) in a counterclockwise direction around its center point To
rotate the image clockwise specify a negative value for angle It makes the output image
large enough to contain the entire rotated image It uses nearest neighbour interpolation
setting the values of pixels in Output image that are outside the rotated image to 0 (zero)
For example
Scale Factor07
Theta30
scaling Image
21
Fig 33 Scaling Image
353 Rotation
It rotates the image by angle degrees in a counterclockwise direction around its
center point To rotate the image clockwise specify a negative value for angle It makes
the output image large enough to contain the entire rotated image It uses nearest
neighbour interpolation setting the values of pixels in Output image that are outside the
rotated image to 0 (zero)
For Example
Theta180
Fig 34 Rotation Image
354 Colour Reduced Image
It creates an indexed image approximation of the RGB image in the array RGB by
dithering the colors in the colormap map The colormap cannot have more than 65536
resized and rotated image
Rotated image
22
colors
For Example
Indexed image with 32 Colors
Fig 35 Colour Reduced Image
355 Blur Image
The image is blurred by using N-D filtering of multidimensional images It filters
the multidimensional array of original image with the multidimensional filter The array
of original image can be logical or a nonsparse numeric array of any class and dimension
The result image has the same size and class as of original image
Fig 36 Blur Image
Color reduced image
50 100 150 200 250 300 350 400 450 500
50
100
150
200
250
300
350
400
450
500
Blurred image
23
356 Flipped Image
It flips the image upside down Flipping is used to invert the image
Fig 37 Flipped Image
357 Cropped Image
It creates an interactive crop image tool associated with the image displayed in the
current figure called the target image The crop image tool is a movable resizable
rectangle that you can position interactively using the mouse When the crop image tool
is active the pointer changes to cross hairs when it is moved over the target image
Using the mouse image to be cropped can be specified by clicking and dragging the
mouse The crop rectangle using the mouse can be moved or resized When sizing and
positioning of the crop rectangle is finished create the cropped image by double-clicking
the left mouse button or by choosing crop image from the context menu Image cropping
returns the cropped image
Flipped image
24
Fig 38 Cropped Image
358 Intensity Transformation Adjust
It maps the intensity values in grayscale image to new values in resultant image
such that 1 of data is saturated at low and high intensity of original image This
increases the contrast of the output image
Fig 39 Intensity Transformation Image
Cropped Image
Intensity Transformation
25
359 Sharpening
Input array values outside the bounds of the array are assumed to equal the nearest
array border value The image is sharpened by using N-D filtering of multidimensional
images It filters the multidimensional array of original image with the multidimensional
filter The array of original image can be logical or a nonsparse numeric array of any
class and dimension The result image has the same size and class as of original image
Fig 310 Sharpened Image
3510 Gaussian Noise and Median Filtering
It adds Gaussian noise to the images Gaussian white noise have constant mean
and variance The noise added image is filtered using Median Filtering Median filtering
is a nonlinear operation often used in image processing to reduce salt and pepper noise
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Sharpened Image
26
Fig 311 Gaussian Noise and Median Filter Image
3511 Histogram of contrast image
It enhances the contrast of images by transforming the values in an intensity
image or the values in the colormap of an indexed image so that the histogram of the
output image approximately matches a specified histogram
Fig 312 Contrast Image
Gaussian Noise
Median Filtering
Contrast Image
27
Fig 313 Histogram of Contrast Image
3512 Speckle noise and Median Filtering
It adds multiplicative noise to the image I using the equation J = I+nI where n is
uniformly distributed random noise with mean 0 and variance v The default for v is 004
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Fig 314 Speckle Noise and Median Filter Image
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Histogram of Contrast Image
0 50 100 150 200 250
Speckle Noise
Median Filtering
28
36 Proposed Algorithm
Step1 Enter two keys a private key and a public key through which the RSA algorithm is
performed
Step2These key are used for encrypting the secret data using chaotic algorithm with
threshold of 2
Step3The secret data is converted into binary format from ASCII code
Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied
Step5The encryption is carried out with the key generated randomly in binary(1times256)
Step6 The 2D logistic substitution and permutation are carried out
Step7The resulting binary sequence is added with the encrypted text in LSB
Step8The image is converted to DNA sequence and transmitted
Step9 Various Attacks have been applied on the resultant image
Step10The inverse process is carried out to retrieve the original image and data
Step11The Performance Metrics have been calculated
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
4
embedded in seemingly innocuous cover messages
Fig 13 Categories of Image Steganography
There are many applications for digital steganography of image including
copyright protection feature tagging and secret communication Copyright notice or
watermark can embedded inside an image to identify it as intellectual property If
someone attempts to use this image without permission we can prove by extracting the
watermark In feature tagging captions annotations time stamps and other descriptive
elements can be embedded inside an image Copying the stegondashimage also copies of the
embedded features and only parties who posses the decoding stego-key will be able to
extract and view the features On the other hand secret communication does not advertise
a covert communication by using steganography Therefore it can avoid scrutiny of the
sender message and recipient This is effective only if the hidden communication is not
detected by the others people In general steganography is two types reversible and
irreversible
5
13 Reversible Data Hiding
Figure 14 Reversible Data Hiding System
Secret Message The secret message or information to hide
Cover File Digital Medium The data or medium which concealed the secret message
Stego File A modified version of cover that contains the secret message
Key Additional secret data that is needed for the embedding and extracting processes
and must be known to both the sender and the recipient
Steganographic Method A steganographic function that takes cover secret message
and key as parameters and produces stego as output
Inverse of Steganographic Method A steganographic function that has stego and key
as parameters and produces secret message as output This is the inverse of method used
in embeding process in the sense that the result of the extracting process is identical to the
input of the embedding process
6
CHAPTER 2
LITERATURE SURVEY
1) Nidhi Sethi Deepika Sharma (2014) ldquoA Novel Method Of Image
Encryption using Logistic Mappingrdquo International Journal of Computer
Science Engineering (IJCSE)
This paper presents a new method to develop secure image-encryption techniques
using a logistics based encryption algorithm In this technique a Haar wavelet transform
was used to decompose the image and decorrelate its pixels into averaging and
differencing components The logistic based encryption algorithm produces a cipher of
the test image that has good diffusion and confusion properties The remaining
components (the differencing components) are compressed using a wavelet transform
Many test images are used to demonstrate the validity of the proposed algorithm The
results of several experiments show that the proposed algorithm for image cryptosystems
provides an efficient and secure approach to real-time image encryption and transmission
To send the keys in secure form steganography will be used Steganographic techniques
allow one party to communicate information to another party without a third party even
knowing that the communication is occurring
Advantages
(i) Efficient approach
(ii) Secure key transmission
(iii) Better image quality
7
2) Jing Wang Guoping Jiang (2013) ldquoA Self-Adaptive Parallel Encryption
Algorithm Based on Discrete 2D-Logistic Maprdquo International Journal of
Modern Nonlinear Theory and Application
A self-adaptive parallel encryption algorithm based on discrete 2D-Logistic
map is developed according to the position scrambling and diffusion of multi-direction in
variable space of spatial chaos The binary sequences b1b2b3bn are obtained according
to the user key in which the binary sequence 0 and 1 denote distribution mode of
processors and the number of binary sequence n denotes cycle number Then the
pseudorandom 2D matrix is generated by 2D-Logistic map and adaptive segmentation is
applied in image matrix and pseudorandom matrix according to the value and the number
of binary sequence The parallel operation is used among blocks to improve efficiency
and meet real-time demand in transmission processes However the pixel permutation is
applied in partitioned matrix through ergodic matrix generated by pseudo-random matrix-
block to decrease the correlation of adjacent pixels Then the pixel substitution is used for
fully diffusing through cipher block chaining mode until n cycles The proposed
algorithm can meet the three requirements of parallel operation in image encryption and
the real-time requirement in transmission processes The security is proved by theoretical
analysis and simulation results
Advantages
1Security is provided
2Effeciency is improved
8
3) LJani Anbarasi GSAnandha Mala Modigari Narendra (2014) ldquoDNA
based Multi-Secret Image Sharingrdquo International Conference on
Information and Communication Technologies (ICICT)
Multiple secret sharing algorithm using the YCH scheme combined with
DNA encoding is proposed focusing at better security Firstly DNA encoding for
multiple images is carried out then the addition of these encoded components by DNA is
performed Secondly the (t n) scheme used the Lagrange interpolation polynomial to
share these DNA scrambled matrices is performed bdquot‟ denotes threshold value and bdquon‟
denotes the number of participants The resulting scrambled images are encrypted into n
shares using Shamir‟s polynomial Thirdly these shares are embedded using a modular
operation Finally bdquot‟ or more shares are pooled which reconstructs the scrambled
matrices and by decoding the DNA scrambled matrices multiple secrets are
reconstructed without loss The simulation results and the security analysis prove that this
algorithm is perfect and produces results with better PSNR value The correlation co-
efficient shows that this also has the ability of resisting various attacks
Advantages
1Security is better
2Resistance against Attack
9
4)Della Babya Jitha Thomasa Gisny Augustinea Elsa Georgea Neenu
Rosia Michaela (2014) ldquoA Novel DWT based Image Securing Method using
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Steganography is a data hiding technique that is widely used in various
information securing applications Steganography transmits data by hiding the existence
of the message so that a viewer cannot identify the transmission of message and hence
not able to decrypt it This work proposes a data securing technique that is used for
hiding multiple color images into a single color image using the Discrete Wavelet
Transform The cover image is split up into R G and B planes Secret images are
embedded into these planes An N-level decomposition of the cover image and the secret
images are done and some frequency components of the same are combined Secret
images are then extracted from the stego image Here the stego image obtained has a less
perceptible changes compared to the original image with high overall security
Advantages
1Less perceptible changes
2Overall security is high
10
5)Prasenjit Dasa Subhrajyoti Deba Nirmalya Kara Baby
Bhattacharyaa(2014)ldquoAn Improved DNA based Dual Cover
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Dual cover steganography is an evolving technique in the field of covert
data transmission This paper focuses on the concept of using a theoretical single stranded
DNA (ssDNA) as a primary cover which is extracted from an inconspicuous cover
image They have analyzed the security loopholes and performance issues of the existing
algorithm and proposed an improved algorithm on the same basisthey use 2D Logistic
map for encrypting the cover imageThen overall encryption is RC43 types of encryption
is generally used Performance of both the algorithms are tested against several visual
and statistical attacks and parameterized in terms of both security and capacity The
comparison shows that the proposed improvements provide better overall security
Advantages
1 Robustness against various attack
2 Performance measure are calculated
3 Data hiding improves security
11
6) Bhavana Agrawal Himani Agrawal ( May-2013) ldquoImplementation of AES
and RSA Using Chaos systemrdquo International Journal of Scientific amp
Engineering Research Vol 4 No 5
This paper presents two cryptographic algorithm AES and RSA Using Chaos
Chaos has attracted much attention in the field of cryptography It describes a system
which is sensitive to initial condition It generates apparently random behavior but at the
same time is completely deterministic Chaos function is used to increase the complexity
and Security of the SystemAES and RSA are the two cryptographic algorithms In AES
we apply the Chaos on S-box where as in RSA we mix the plaintext with Chaos sequence
First then apply for encryption and decryption After Implementing AES and RSA they
compare both the technique on the basis of speed
Advantages
1Chaos function is used to improve complexity
2The speed has been improved with combined technique of AES and RSA along with
chaos technique
12
7) Pahrul Irfan Yudi Prayudi Imam Riadi ( August 2015) ldquo Image
Encryption using Combination of Chaotic System and Rivers Shamir
Adleman (RSA)rdquo International Journal of Computer Applications Vol 123
No6
Security and confidentiality of data or information at the present time has
become an important concern Advanced methods for secure transmission storage and
retrieval of digital images are increasingly needed for a number of military medical
homeland security and other applications Various kinds of techniques for increase
security data or information already is developed one common way is by cryptographic
techniques Cryptography is science to maintain the security of the message by changing
data or information into a different form so the message cannot be recognized To
compensate for increasing computing speeds increases it takes more than one encryption
algorithm to improve security of digital images One way is by using algorithms to
double cryptography do encryption and decryption Cryptographic algorithm often used
today and the proven strength specially the digital image is Algorithm with Chaos
system To improve security at the image then we use Additional algorithms namely
Rivers algorithm Shamir Adleman (RSA) which known as the standard of cryptography
algorithms This research aims to optimize security bitmap image format by combining
the two algorithms namely Chaos-based algorithms and RSA algorithm into one
application Experiments conducted show that the proposed algorithm possesses robust
security features such as fairly uniform distribution high sensitivity to both keys and
plain images almost ideal entropy and the ability to highly de-correlate adjacent pixels
in the cipher images Furthermore it has a large key space and transform image to pure
text file which greatly increases its security for image encryption
Advantages
1 It aims to optimize security bitmap image format by combining the two algorithms
namely Chaos-based algorithms and RSA algorithm into one application
13
8) Weiming Zhang KedeMa NenghaiYu(2013) ldquo Reversibility improved
data hiding in encrypted imagesrdquo School of Information Science and
Technology
A novel reversible data hiding technique in encrypted images is presented in this
paper Instead of embedding data in encrypted images directly some pixels are estimated
before encryption so that additional data can be embedded in the estimating errors A
bench mark encryption algorithm (eg AES) is applied to the rest pixels of the image and
a special encryption scheme is designed to encrypt the estimating errors Without the
encryption key one cannot get access to the original image However provided with the
data hiding key only he can embed in or extract from the encrypted image additional data
without knowledge about the original image Moreover the data extraction and image
recovery are free of errors for all images Experiments demonstrate the feasibility and
efficiency of the proposed method especially in aspect of embedding rate versus Peak
Signal-to-Noise Ratio (PSNR)
The paper proposes a novel method to significantly improve the performance by
reversing the order of encryption and vacating room In the light of this idea we empty
out room prior to image encryption by shifting the histogram of estimating errors of some
pixels and the emptied out room will be used for data hiding The proposed method is
composed of four primary steps vacating room and encrypting image data hiding in the
encrypted image data extraction and image recovery Two different schemes extraction
before decryption and decryption before extraction are raised to cope with different
applications
Advantages
(i) Achieves excellent performance in three aspects complete reversibility PSNR
under given embedding rate separability between data higher extraction and
image decryption
14
CHAPTER 3
PROPOSED METHODOLOGY
The proposed data hiding scheme aims at the security of the hidden data
Embedding is performed in spatial domain The data to be embedded is converted into
binary form from ASCII code using chaos encryption and is embedded into the cover
image obtained after 2D logistic map This embedded image is secured using symmetric
key (K1)They are converted into DNA sequence to provide additional level of security
The hidden data can be extracted from the cover image accurately with the help of
decryption techniques and secret key (K1) The cover image can also be extracted
without any distortion The fig 31 shows the workflow
Fig 31 Work Flow Diagram
SECRET DATA
COVER IMAGE
CHAOTIC
ENCRYPTION
ENCRY 2D LOGISTIC
ENCRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
CHAOTIC
DECRYPTION
ENCRY
SECRET DATA
COVER IMAGE 2D LOGISTIC
DECRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
15
31 Chaotic Encryption
Chaotic cryptography is the application of the mathematical chaos theory to the
practice of the cryptography the study or techniques used to privately and securely
transmit information with the presence of an third-party or adversary The use of chaos
or randomness in cryptography has long been sought after by entities wanting a new way
to encrypt messages However because of the lack of thorough provable security
properties and low acceptable performance chaotic cryptography has encountered
setbacksIn order to use chaos theory acceptably in cryptography they must first be
mapped to each other Properties in chaotic systems and cryptographic primitives share
unique characteristics that allow for the chaotic systems to be applied to cryptography If
chaotic parameters as well as cryptographic keys can be mapped symmetrically or
mapped to produce acceptable and functional outputs it will make it next to impossible
for an adversary to find the outputs without any knowledge the initial values Since
chaotic maps in a real life scenario require a set of numbers that are limited they may in
fact have no real purpose in a cryptosystem if the chaotic behavior can be predicted To
counter this possibility there exists simple to advanced ciphers Chaos theory used in
cryptosystems for commercial implementation has proven to be unsuccessful mainly
because a chaos theories‟ requirement to use intervals of real numbers Given enough
resources and time an adversary could be able to predict functional outcomes Since
chaotic cryptosystems have no root in number theory this would make it difficult or
impossible to implement therefore impractical
32 The RSA Algorithm
The RSA cryptosystem named after its inventors R Rivest A Shamir and L
Adleman is the most widely used public key Cryptosystem It may be used to provide
both secrecy and digital signatures and its security is based on the intractability of the
integer factorizationThe RSA algorithm involves three steps key generation encryption
and decryption
16
321 Key Generation
RSA involves a public key and a private key The public key can be known to
everyone and is used for encrypting messages Messages encrypted with the public key
can only be decrypted in a reasonable amount of time using the private key The keys for
the RSA algorithm are generated the following way To generate the two keys choose
two random large prime numbers p and q For maximum security choose p and q of
equal length Then randomly choose the encryption key e such that e and ( p minus1) (q minus1)
are relatively prime Finally use the extended Euclidean algorithm to compute the
decryption key d such that
d= e-1
mod ( (p-1) (q-1))
Note that d and n are also relatively prime The numbers e and K are the public
key the number d is the private key The two primes p and q are no longer needed They
should be discarded but never revealed
322 Encryption
Firstly receiver transmits her public key (n e) to sender and keeps the private key
secret If sender wishes to send message M to receiver Sender change the message M in
to integer m such that 0 le mltn Then sender computes the cipher text c corresponding to
Cequiv me
(mod n)
323 Decryption
Receiver can recover M from c by using private key exponent d via computing
M equiv cd
(mod n)
17
Algorithm
1Select any two prime numbers say (pq)
2Compute n=pq and also compute empty(119899)=(p-1)(q-1)
3Choose e such that 1ltelt empty(119899)
4Choose d such that (de)mod empty(119899)=1
5Public key is (en) and Private key is (dn)
6 If egt=2 then check i==1 if so return 1 else return 0
7In a iteration check for e(i)==1 if so take mod function of message with n
8Message is converted to cipher text in ASCII form with the key generated
9The cipher data in ASCII form is converted to binary form
33 2D Logistic Encryption
The chaotic system is a deterministic nonlinear system It possesses a varied
characteristics such as high sensitivity to initial conditions and system parameters
random-like behaviors and so forth Chaotic sequences produced by chaotic maps are
pseudo-random sequences their structures are very complex and difficult to be analyzed
and predicted In other words chaotic systems can improve the security of encryption
systems Thus it is advisable to encrypt digital image with chaotic systems There are
two chaotic maps one is logistic map and the other is 2D logistic map In the proposed
work 2D logistic map is used
Logistic map is an example for chaotic map and it is described as follows
x(n+1)=μ x(n)(1minusx(n))
μ is a positive constant sometimes known as the biotic potential gives the so-called
logistic map x(n) is series of values used to plot Logistic mapwhere μ isin [04] x(n) isin
(01) and n = 01 2hellip The research result shows that the system is in chaotic state
under the condition that 356994 lt μ le 4
18
2D logistic map is described in as follows
119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2
119910119894+1 = 1205832 lowast 119910119894(1 minus 119910119894) + 1205732((119909119894)2 + 119909119894 lowast 119910119894)
Where z(xy) is the logistic map 1205831 and 1205832 are positive constant sometimes known as the
biotic potential gives the so-called logistic map x be the position of images in x axis
and y be the position of images in y axis 1205731 and 1205732 are the correlation constantsWhen
275 lt μ1 le 34 275 lt μ2 le 345 015 lt 1205731le 021 and 013 lt 1205732 le 015 the system is in
chaotic state and can generate two chaotic sequences in the region (01] Due to the
system parameter γ1 and γ2 which have smaller value range we set γ1 = 017 and γ2 =
014 other parameters can be seen as secret keys
Algorithm
1A random key is generated in binary form ( 1times256) and it is stored in a array
2The random key generated is translated to map format using block processing (4times4)
3 The row and column wise transformation is carried out
4The key is now used to encrypt the cover image
52D logistic image undergoes substitution and permutation (column and row wise
shuffling is done)
34 DNA Sequence
A single DNA sequence is made up of four nucleic acid
bases A (adenine) C (cytosine) G (guanine) and T (thymine) where A and T are
complements and C and G are complements Let binary number 0 and 1 be
complements so 00 and 11 are complements and 01 and 10 are complements Thus we
can use these four bases A T G and C to encode 01 10 00 and 11 respectively The
encoding method still satisfies the Watson-Crick complement rule Usually each pixel
value of the 8 bit grey image can be expressed to 8 bits binary stream The binary stream
can be encoded to a DNA sequence whose length is 4 For example if the first pixel
19
value of the original image is 75 convert it into a binary stream [01001011] By using the
above DNA encoding rule to encode the stream we can get a DNA sequence [AGTC]
whereas we use A T G and C to express 01 10 00 and 11 respectively We can get a
binary sequence [01001011]
35 Attacks
The steganographic algorithm is used to embed secret messages into cover
image To obtain stego image while exchanging these stego-image through the public
communication channel various attacks have been made The are generally classified
into two types intentional or unintentional attacks Examples of unintentional attacks are
transmission errors lossy compression and changing the visual properties of the stego
document Intentional attacks on the other hand are deliberate attempts to distinguish
stego-objects from unmodified objects and thus detect the presence of covert
communication Attack methods generally exploit the fact that embedding information
usually changes the statistical properties of the objects compared to typical unmodified
objects In this proposed algorithm various attacks have been applied on the encrypted
image They are as follows
1)Shearing
2) Image Scaling
3) Image Rotating
4) Image color reduction
5) Image blurred
6) Image flip
7) cropping and intensity transformation
8) Image sharpening
9) Gaussian Noise and filtering
10) Image Contrast
11) Speckle Noise and Filtering
20
351 Shearing
The image is resized with the scale factor Resized image is rotated with
angle(theta)Finally spatial transformation from control point pairs is implemented
For example
Scale factor 09
Theta10
Fig 32 Shearing Image
352 Image Scaling
It resizes the image with a scale factor and rotation is performed It rotates the
image by angle (degrees) in a counterclockwise direction around its center point To
rotate the image clockwise specify a negative value for angle It makes the output image
large enough to contain the entire rotated image It uses nearest neighbour interpolation
setting the values of pixels in Output image that are outside the rotated image to 0 (zero)
For example
Scale Factor07
Theta30
scaling Image
21
Fig 33 Scaling Image
353 Rotation
It rotates the image by angle degrees in a counterclockwise direction around its
center point To rotate the image clockwise specify a negative value for angle It makes
the output image large enough to contain the entire rotated image It uses nearest
neighbour interpolation setting the values of pixels in Output image that are outside the
rotated image to 0 (zero)
For Example
Theta180
Fig 34 Rotation Image
354 Colour Reduced Image
It creates an indexed image approximation of the RGB image in the array RGB by
dithering the colors in the colormap map The colormap cannot have more than 65536
resized and rotated image
Rotated image
22
colors
For Example
Indexed image with 32 Colors
Fig 35 Colour Reduced Image
355 Blur Image
The image is blurred by using N-D filtering of multidimensional images It filters
the multidimensional array of original image with the multidimensional filter The array
of original image can be logical or a nonsparse numeric array of any class and dimension
The result image has the same size and class as of original image
Fig 36 Blur Image
Color reduced image
50 100 150 200 250 300 350 400 450 500
50
100
150
200
250
300
350
400
450
500
Blurred image
23
356 Flipped Image
It flips the image upside down Flipping is used to invert the image
Fig 37 Flipped Image
357 Cropped Image
It creates an interactive crop image tool associated with the image displayed in the
current figure called the target image The crop image tool is a movable resizable
rectangle that you can position interactively using the mouse When the crop image tool
is active the pointer changes to cross hairs when it is moved over the target image
Using the mouse image to be cropped can be specified by clicking and dragging the
mouse The crop rectangle using the mouse can be moved or resized When sizing and
positioning of the crop rectangle is finished create the cropped image by double-clicking
the left mouse button or by choosing crop image from the context menu Image cropping
returns the cropped image
Flipped image
24
Fig 38 Cropped Image
358 Intensity Transformation Adjust
It maps the intensity values in grayscale image to new values in resultant image
such that 1 of data is saturated at low and high intensity of original image This
increases the contrast of the output image
Fig 39 Intensity Transformation Image
Cropped Image
Intensity Transformation
25
359 Sharpening
Input array values outside the bounds of the array are assumed to equal the nearest
array border value The image is sharpened by using N-D filtering of multidimensional
images It filters the multidimensional array of original image with the multidimensional
filter The array of original image can be logical or a nonsparse numeric array of any
class and dimension The result image has the same size and class as of original image
Fig 310 Sharpened Image
3510 Gaussian Noise and Median Filtering
It adds Gaussian noise to the images Gaussian white noise have constant mean
and variance The noise added image is filtered using Median Filtering Median filtering
is a nonlinear operation often used in image processing to reduce salt and pepper noise
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Sharpened Image
26
Fig 311 Gaussian Noise and Median Filter Image
3511 Histogram of contrast image
It enhances the contrast of images by transforming the values in an intensity
image or the values in the colormap of an indexed image so that the histogram of the
output image approximately matches a specified histogram
Fig 312 Contrast Image
Gaussian Noise
Median Filtering
Contrast Image
27
Fig 313 Histogram of Contrast Image
3512 Speckle noise and Median Filtering
It adds multiplicative noise to the image I using the equation J = I+nI where n is
uniformly distributed random noise with mean 0 and variance v The default for v is 004
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Fig 314 Speckle Noise and Median Filter Image
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Histogram of Contrast Image
0 50 100 150 200 250
Speckle Noise
Median Filtering
28
36 Proposed Algorithm
Step1 Enter two keys a private key and a public key through which the RSA algorithm is
performed
Step2These key are used for encrypting the secret data using chaotic algorithm with
threshold of 2
Step3The secret data is converted into binary format from ASCII code
Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied
Step5The encryption is carried out with the key generated randomly in binary(1times256)
Step6 The 2D logistic substitution and permutation are carried out
Step7The resulting binary sequence is added with the encrypted text in LSB
Step8The image is converted to DNA sequence and transmitted
Step9 Various Attacks have been applied on the resultant image
Step10The inverse process is carried out to retrieve the original image and data
Step11The Performance Metrics have been calculated
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
5
13 Reversible Data Hiding
Figure 14 Reversible Data Hiding System
Secret Message The secret message or information to hide
Cover File Digital Medium The data or medium which concealed the secret message
Stego File A modified version of cover that contains the secret message
Key Additional secret data that is needed for the embedding and extracting processes
and must be known to both the sender and the recipient
Steganographic Method A steganographic function that takes cover secret message
and key as parameters and produces stego as output
Inverse of Steganographic Method A steganographic function that has stego and key
as parameters and produces secret message as output This is the inverse of method used
in embeding process in the sense that the result of the extracting process is identical to the
input of the embedding process
6
CHAPTER 2
LITERATURE SURVEY
1) Nidhi Sethi Deepika Sharma (2014) ldquoA Novel Method Of Image
Encryption using Logistic Mappingrdquo International Journal of Computer
Science Engineering (IJCSE)
This paper presents a new method to develop secure image-encryption techniques
using a logistics based encryption algorithm In this technique a Haar wavelet transform
was used to decompose the image and decorrelate its pixels into averaging and
differencing components The logistic based encryption algorithm produces a cipher of
the test image that has good diffusion and confusion properties The remaining
components (the differencing components) are compressed using a wavelet transform
Many test images are used to demonstrate the validity of the proposed algorithm The
results of several experiments show that the proposed algorithm for image cryptosystems
provides an efficient and secure approach to real-time image encryption and transmission
To send the keys in secure form steganography will be used Steganographic techniques
allow one party to communicate information to another party without a third party even
knowing that the communication is occurring
Advantages
(i) Efficient approach
(ii) Secure key transmission
(iii) Better image quality
7
2) Jing Wang Guoping Jiang (2013) ldquoA Self-Adaptive Parallel Encryption
Algorithm Based on Discrete 2D-Logistic Maprdquo International Journal of
Modern Nonlinear Theory and Application
A self-adaptive parallel encryption algorithm based on discrete 2D-Logistic
map is developed according to the position scrambling and diffusion of multi-direction in
variable space of spatial chaos The binary sequences b1b2b3bn are obtained according
to the user key in which the binary sequence 0 and 1 denote distribution mode of
processors and the number of binary sequence n denotes cycle number Then the
pseudorandom 2D matrix is generated by 2D-Logistic map and adaptive segmentation is
applied in image matrix and pseudorandom matrix according to the value and the number
of binary sequence The parallel operation is used among blocks to improve efficiency
and meet real-time demand in transmission processes However the pixel permutation is
applied in partitioned matrix through ergodic matrix generated by pseudo-random matrix-
block to decrease the correlation of adjacent pixels Then the pixel substitution is used for
fully diffusing through cipher block chaining mode until n cycles The proposed
algorithm can meet the three requirements of parallel operation in image encryption and
the real-time requirement in transmission processes The security is proved by theoretical
analysis and simulation results
Advantages
1Security is provided
2Effeciency is improved
8
3) LJani Anbarasi GSAnandha Mala Modigari Narendra (2014) ldquoDNA
based Multi-Secret Image Sharingrdquo International Conference on
Information and Communication Technologies (ICICT)
Multiple secret sharing algorithm using the YCH scheme combined with
DNA encoding is proposed focusing at better security Firstly DNA encoding for
multiple images is carried out then the addition of these encoded components by DNA is
performed Secondly the (t n) scheme used the Lagrange interpolation polynomial to
share these DNA scrambled matrices is performed bdquot‟ denotes threshold value and bdquon‟
denotes the number of participants The resulting scrambled images are encrypted into n
shares using Shamir‟s polynomial Thirdly these shares are embedded using a modular
operation Finally bdquot‟ or more shares are pooled which reconstructs the scrambled
matrices and by decoding the DNA scrambled matrices multiple secrets are
reconstructed without loss The simulation results and the security analysis prove that this
algorithm is perfect and produces results with better PSNR value The correlation co-
efficient shows that this also has the ability of resisting various attacks
Advantages
1Security is better
2Resistance against Attack
9
4)Della Babya Jitha Thomasa Gisny Augustinea Elsa Georgea Neenu
Rosia Michaela (2014) ldquoA Novel DWT based Image Securing Method using
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Steganography is a data hiding technique that is widely used in various
information securing applications Steganography transmits data by hiding the existence
of the message so that a viewer cannot identify the transmission of message and hence
not able to decrypt it This work proposes a data securing technique that is used for
hiding multiple color images into a single color image using the Discrete Wavelet
Transform The cover image is split up into R G and B planes Secret images are
embedded into these planes An N-level decomposition of the cover image and the secret
images are done and some frequency components of the same are combined Secret
images are then extracted from the stego image Here the stego image obtained has a less
perceptible changes compared to the original image with high overall security
Advantages
1Less perceptible changes
2Overall security is high
10
5)Prasenjit Dasa Subhrajyoti Deba Nirmalya Kara Baby
Bhattacharyaa(2014)ldquoAn Improved DNA based Dual Cover
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Dual cover steganography is an evolving technique in the field of covert
data transmission This paper focuses on the concept of using a theoretical single stranded
DNA (ssDNA) as a primary cover which is extracted from an inconspicuous cover
image They have analyzed the security loopholes and performance issues of the existing
algorithm and proposed an improved algorithm on the same basisthey use 2D Logistic
map for encrypting the cover imageThen overall encryption is RC43 types of encryption
is generally used Performance of both the algorithms are tested against several visual
and statistical attacks and parameterized in terms of both security and capacity The
comparison shows that the proposed improvements provide better overall security
Advantages
1 Robustness against various attack
2 Performance measure are calculated
3 Data hiding improves security
11
6) Bhavana Agrawal Himani Agrawal ( May-2013) ldquoImplementation of AES
and RSA Using Chaos systemrdquo International Journal of Scientific amp
Engineering Research Vol 4 No 5
This paper presents two cryptographic algorithm AES and RSA Using Chaos
Chaos has attracted much attention in the field of cryptography It describes a system
which is sensitive to initial condition It generates apparently random behavior but at the
same time is completely deterministic Chaos function is used to increase the complexity
and Security of the SystemAES and RSA are the two cryptographic algorithms In AES
we apply the Chaos on S-box where as in RSA we mix the plaintext with Chaos sequence
First then apply for encryption and decryption After Implementing AES and RSA they
compare both the technique on the basis of speed
Advantages
1Chaos function is used to improve complexity
2The speed has been improved with combined technique of AES and RSA along with
chaos technique
12
7) Pahrul Irfan Yudi Prayudi Imam Riadi ( August 2015) ldquo Image
Encryption using Combination of Chaotic System and Rivers Shamir
Adleman (RSA)rdquo International Journal of Computer Applications Vol 123
No6
Security and confidentiality of data or information at the present time has
become an important concern Advanced methods for secure transmission storage and
retrieval of digital images are increasingly needed for a number of military medical
homeland security and other applications Various kinds of techniques for increase
security data or information already is developed one common way is by cryptographic
techniques Cryptography is science to maintain the security of the message by changing
data or information into a different form so the message cannot be recognized To
compensate for increasing computing speeds increases it takes more than one encryption
algorithm to improve security of digital images One way is by using algorithms to
double cryptography do encryption and decryption Cryptographic algorithm often used
today and the proven strength specially the digital image is Algorithm with Chaos
system To improve security at the image then we use Additional algorithms namely
Rivers algorithm Shamir Adleman (RSA) which known as the standard of cryptography
algorithms This research aims to optimize security bitmap image format by combining
the two algorithms namely Chaos-based algorithms and RSA algorithm into one
application Experiments conducted show that the proposed algorithm possesses robust
security features such as fairly uniform distribution high sensitivity to both keys and
plain images almost ideal entropy and the ability to highly de-correlate adjacent pixels
in the cipher images Furthermore it has a large key space and transform image to pure
text file which greatly increases its security for image encryption
Advantages
1 It aims to optimize security bitmap image format by combining the two algorithms
namely Chaos-based algorithms and RSA algorithm into one application
13
8) Weiming Zhang KedeMa NenghaiYu(2013) ldquo Reversibility improved
data hiding in encrypted imagesrdquo School of Information Science and
Technology
A novel reversible data hiding technique in encrypted images is presented in this
paper Instead of embedding data in encrypted images directly some pixels are estimated
before encryption so that additional data can be embedded in the estimating errors A
bench mark encryption algorithm (eg AES) is applied to the rest pixels of the image and
a special encryption scheme is designed to encrypt the estimating errors Without the
encryption key one cannot get access to the original image However provided with the
data hiding key only he can embed in or extract from the encrypted image additional data
without knowledge about the original image Moreover the data extraction and image
recovery are free of errors for all images Experiments demonstrate the feasibility and
efficiency of the proposed method especially in aspect of embedding rate versus Peak
Signal-to-Noise Ratio (PSNR)
The paper proposes a novel method to significantly improve the performance by
reversing the order of encryption and vacating room In the light of this idea we empty
out room prior to image encryption by shifting the histogram of estimating errors of some
pixels and the emptied out room will be used for data hiding The proposed method is
composed of four primary steps vacating room and encrypting image data hiding in the
encrypted image data extraction and image recovery Two different schemes extraction
before decryption and decryption before extraction are raised to cope with different
applications
Advantages
(i) Achieves excellent performance in three aspects complete reversibility PSNR
under given embedding rate separability between data higher extraction and
image decryption
14
CHAPTER 3
PROPOSED METHODOLOGY
The proposed data hiding scheme aims at the security of the hidden data
Embedding is performed in spatial domain The data to be embedded is converted into
binary form from ASCII code using chaos encryption and is embedded into the cover
image obtained after 2D logistic map This embedded image is secured using symmetric
key (K1)They are converted into DNA sequence to provide additional level of security
The hidden data can be extracted from the cover image accurately with the help of
decryption techniques and secret key (K1) The cover image can also be extracted
without any distortion The fig 31 shows the workflow
Fig 31 Work Flow Diagram
SECRET DATA
COVER IMAGE
CHAOTIC
ENCRYPTION
ENCRY 2D LOGISTIC
ENCRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
CHAOTIC
DECRYPTION
ENCRY
SECRET DATA
COVER IMAGE 2D LOGISTIC
DECRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
15
31 Chaotic Encryption
Chaotic cryptography is the application of the mathematical chaos theory to the
practice of the cryptography the study or techniques used to privately and securely
transmit information with the presence of an third-party or adversary The use of chaos
or randomness in cryptography has long been sought after by entities wanting a new way
to encrypt messages However because of the lack of thorough provable security
properties and low acceptable performance chaotic cryptography has encountered
setbacksIn order to use chaos theory acceptably in cryptography they must first be
mapped to each other Properties in chaotic systems and cryptographic primitives share
unique characteristics that allow for the chaotic systems to be applied to cryptography If
chaotic parameters as well as cryptographic keys can be mapped symmetrically or
mapped to produce acceptable and functional outputs it will make it next to impossible
for an adversary to find the outputs without any knowledge the initial values Since
chaotic maps in a real life scenario require a set of numbers that are limited they may in
fact have no real purpose in a cryptosystem if the chaotic behavior can be predicted To
counter this possibility there exists simple to advanced ciphers Chaos theory used in
cryptosystems for commercial implementation has proven to be unsuccessful mainly
because a chaos theories‟ requirement to use intervals of real numbers Given enough
resources and time an adversary could be able to predict functional outcomes Since
chaotic cryptosystems have no root in number theory this would make it difficult or
impossible to implement therefore impractical
32 The RSA Algorithm
The RSA cryptosystem named after its inventors R Rivest A Shamir and L
Adleman is the most widely used public key Cryptosystem It may be used to provide
both secrecy and digital signatures and its security is based on the intractability of the
integer factorizationThe RSA algorithm involves three steps key generation encryption
and decryption
16
321 Key Generation
RSA involves a public key and a private key The public key can be known to
everyone and is used for encrypting messages Messages encrypted with the public key
can only be decrypted in a reasonable amount of time using the private key The keys for
the RSA algorithm are generated the following way To generate the two keys choose
two random large prime numbers p and q For maximum security choose p and q of
equal length Then randomly choose the encryption key e such that e and ( p minus1) (q minus1)
are relatively prime Finally use the extended Euclidean algorithm to compute the
decryption key d such that
d= e-1
mod ( (p-1) (q-1))
Note that d and n are also relatively prime The numbers e and K are the public
key the number d is the private key The two primes p and q are no longer needed They
should be discarded but never revealed
322 Encryption
Firstly receiver transmits her public key (n e) to sender and keeps the private key
secret If sender wishes to send message M to receiver Sender change the message M in
to integer m such that 0 le mltn Then sender computes the cipher text c corresponding to
Cequiv me
(mod n)
323 Decryption
Receiver can recover M from c by using private key exponent d via computing
M equiv cd
(mod n)
17
Algorithm
1Select any two prime numbers say (pq)
2Compute n=pq and also compute empty(119899)=(p-1)(q-1)
3Choose e such that 1ltelt empty(119899)
4Choose d such that (de)mod empty(119899)=1
5Public key is (en) and Private key is (dn)
6 If egt=2 then check i==1 if so return 1 else return 0
7In a iteration check for e(i)==1 if so take mod function of message with n
8Message is converted to cipher text in ASCII form with the key generated
9The cipher data in ASCII form is converted to binary form
33 2D Logistic Encryption
The chaotic system is a deterministic nonlinear system It possesses a varied
characteristics such as high sensitivity to initial conditions and system parameters
random-like behaviors and so forth Chaotic sequences produced by chaotic maps are
pseudo-random sequences their structures are very complex and difficult to be analyzed
and predicted In other words chaotic systems can improve the security of encryption
systems Thus it is advisable to encrypt digital image with chaotic systems There are
two chaotic maps one is logistic map and the other is 2D logistic map In the proposed
work 2D logistic map is used
Logistic map is an example for chaotic map and it is described as follows
x(n+1)=μ x(n)(1minusx(n))
μ is a positive constant sometimes known as the biotic potential gives the so-called
logistic map x(n) is series of values used to plot Logistic mapwhere μ isin [04] x(n) isin
(01) and n = 01 2hellip The research result shows that the system is in chaotic state
under the condition that 356994 lt μ le 4
18
2D logistic map is described in as follows
119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2
119910119894+1 = 1205832 lowast 119910119894(1 minus 119910119894) + 1205732((119909119894)2 + 119909119894 lowast 119910119894)
Where z(xy) is the logistic map 1205831 and 1205832 are positive constant sometimes known as the
biotic potential gives the so-called logistic map x be the position of images in x axis
and y be the position of images in y axis 1205731 and 1205732 are the correlation constantsWhen
275 lt μ1 le 34 275 lt μ2 le 345 015 lt 1205731le 021 and 013 lt 1205732 le 015 the system is in
chaotic state and can generate two chaotic sequences in the region (01] Due to the
system parameter γ1 and γ2 which have smaller value range we set γ1 = 017 and γ2 =
014 other parameters can be seen as secret keys
Algorithm
1A random key is generated in binary form ( 1times256) and it is stored in a array
2The random key generated is translated to map format using block processing (4times4)
3 The row and column wise transformation is carried out
4The key is now used to encrypt the cover image
52D logistic image undergoes substitution and permutation (column and row wise
shuffling is done)
34 DNA Sequence
A single DNA sequence is made up of four nucleic acid
bases A (adenine) C (cytosine) G (guanine) and T (thymine) where A and T are
complements and C and G are complements Let binary number 0 and 1 be
complements so 00 and 11 are complements and 01 and 10 are complements Thus we
can use these four bases A T G and C to encode 01 10 00 and 11 respectively The
encoding method still satisfies the Watson-Crick complement rule Usually each pixel
value of the 8 bit grey image can be expressed to 8 bits binary stream The binary stream
can be encoded to a DNA sequence whose length is 4 For example if the first pixel
19
value of the original image is 75 convert it into a binary stream [01001011] By using the
above DNA encoding rule to encode the stream we can get a DNA sequence [AGTC]
whereas we use A T G and C to express 01 10 00 and 11 respectively We can get a
binary sequence [01001011]
35 Attacks
The steganographic algorithm is used to embed secret messages into cover
image To obtain stego image while exchanging these stego-image through the public
communication channel various attacks have been made The are generally classified
into two types intentional or unintentional attacks Examples of unintentional attacks are
transmission errors lossy compression and changing the visual properties of the stego
document Intentional attacks on the other hand are deliberate attempts to distinguish
stego-objects from unmodified objects and thus detect the presence of covert
communication Attack methods generally exploit the fact that embedding information
usually changes the statistical properties of the objects compared to typical unmodified
objects In this proposed algorithm various attacks have been applied on the encrypted
image They are as follows
1)Shearing
2) Image Scaling
3) Image Rotating
4) Image color reduction
5) Image blurred
6) Image flip
7) cropping and intensity transformation
8) Image sharpening
9) Gaussian Noise and filtering
10) Image Contrast
11) Speckle Noise and Filtering
20
351 Shearing
The image is resized with the scale factor Resized image is rotated with
angle(theta)Finally spatial transformation from control point pairs is implemented
For example
Scale factor 09
Theta10
Fig 32 Shearing Image
352 Image Scaling
It resizes the image with a scale factor and rotation is performed It rotates the
image by angle (degrees) in a counterclockwise direction around its center point To
rotate the image clockwise specify a negative value for angle It makes the output image
large enough to contain the entire rotated image It uses nearest neighbour interpolation
setting the values of pixels in Output image that are outside the rotated image to 0 (zero)
For example
Scale Factor07
Theta30
scaling Image
21
Fig 33 Scaling Image
353 Rotation
It rotates the image by angle degrees in a counterclockwise direction around its
center point To rotate the image clockwise specify a negative value for angle It makes
the output image large enough to contain the entire rotated image It uses nearest
neighbour interpolation setting the values of pixels in Output image that are outside the
rotated image to 0 (zero)
For Example
Theta180
Fig 34 Rotation Image
354 Colour Reduced Image
It creates an indexed image approximation of the RGB image in the array RGB by
dithering the colors in the colormap map The colormap cannot have more than 65536
resized and rotated image
Rotated image
22
colors
For Example
Indexed image with 32 Colors
Fig 35 Colour Reduced Image
355 Blur Image
The image is blurred by using N-D filtering of multidimensional images It filters
the multidimensional array of original image with the multidimensional filter The array
of original image can be logical or a nonsparse numeric array of any class and dimension
The result image has the same size and class as of original image
Fig 36 Blur Image
Color reduced image
50 100 150 200 250 300 350 400 450 500
50
100
150
200
250
300
350
400
450
500
Blurred image
23
356 Flipped Image
It flips the image upside down Flipping is used to invert the image
Fig 37 Flipped Image
357 Cropped Image
It creates an interactive crop image tool associated with the image displayed in the
current figure called the target image The crop image tool is a movable resizable
rectangle that you can position interactively using the mouse When the crop image tool
is active the pointer changes to cross hairs when it is moved over the target image
Using the mouse image to be cropped can be specified by clicking and dragging the
mouse The crop rectangle using the mouse can be moved or resized When sizing and
positioning of the crop rectangle is finished create the cropped image by double-clicking
the left mouse button or by choosing crop image from the context menu Image cropping
returns the cropped image
Flipped image
24
Fig 38 Cropped Image
358 Intensity Transformation Adjust
It maps the intensity values in grayscale image to new values in resultant image
such that 1 of data is saturated at low and high intensity of original image This
increases the contrast of the output image
Fig 39 Intensity Transformation Image
Cropped Image
Intensity Transformation
25
359 Sharpening
Input array values outside the bounds of the array are assumed to equal the nearest
array border value The image is sharpened by using N-D filtering of multidimensional
images It filters the multidimensional array of original image with the multidimensional
filter The array of original image can be logical or a nonsparse numeric array of any
class and dimension The result image has the same size and class as of original image
Fig 310 Sharpened Image
3510 Gaussian Noise and Median Filtering
It adds Gaussian noise to the images Gaussian white noise have constant mean
and variance The noise added image is filtered using Median Filtering Median filtering
is a nonlinear operation often used in image processing to reduce salt and pepper noise
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Sharpened Image
26
Fig 311 Gaussian Noise and Median Filter Image
3511 Histogram of contrast image
It enhances the contrast of images by transforming the values in an intensity
image or the values in the colormap of an indexed image so that the histogram of the
output image approximately matches a specified histogram
Fig 312 Contrast Image
Gaussian Noise
Median Filtering
Contrast Image
27
Fig 313 Histogram of Contrast Image
3512 Speckle noise and Median Filtering
It adds multiplicative noise to the image I using the equation J = I+nI where n is
uniformly distributed random noise with mean 0 and variance v The default for v is 004
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Fig 314 Speckle Noise and Median Filter Image
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Histogram of Contrast Image
0 50 100 150 200 250
Speckle Noise
Median Filtering
28
36 Proposed Algorithm
Step1 Enter two keys a private key and a public key through which the RSA algorithm is
performed
Step2These key are used for encrypting the secret data using chaotic algorithm with
threshold of 2
Step3The secret data is converted into binary format from ASCII code
Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied
Step5The encryption is carried out with the key generated randomly in binary(1times256)
Step6 The 2D logistic substitution and permutation are carried out
Step7The resulting binary sequence is added with the encrypted text in LSB
Step8The image is converted to DNA sequence and transmitted
Step9 Various Attacks have been applied on the resultant image
Step10The inverse process is carried out to retrieve the original image and data
Step11The Performance Metrics have been calculated
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
6
CHAPTER 2
LITERATURE SURVEY
1) Nidhi Sethi Deepika Sharma (2014) ldquoA Novel Method Of Image
Encryption using Logistic Mappingrdquo International Journal of Computer
Science Engineering (IJCSE)
This paper presents a new method to develop secure image-encryption techniques
using a logistics based encryption algorithm In this technique a Haar wavelet transform
was used to decompose the image and decorrelate its pixels into averaging and
differencing components The logistic based encryption algorithm produces a cipher of
the test image that has good diffusion and confusion properties The remaining
components (the differencing components) are compressed using a wavelet transform
Many test images are used to demonstrate the validity of the proposed algorithm The
results of several experiments show that the proposed algorithm for image cryptosystems
provides an efficient and secure approach to real-time image encryption and transmission
To send the keys in secure form steganography will be used Steganographic techniques
allow one party to communicate information to another party without a third party even
knowing that the communication is occurring
Advantages
(i) Efficient approach
(ii) Secure key transmission
(iii) Better image quality
7
2) Jing Wang Guoping Jiang (2013) ldquoA Self-Adaptive Parallel Encryption
Algorithm Based on Discrete 2D-Logistic Maprdquo International Journal of
Modern Nonlinear Theory and Application
A self-adaptive parallel encryption algorithm based on discrete 2D-Logistic
map is developed according to the position scrambling and diffusion of multi-direction in
variable space of spatial chaos The binary sequences b1b2b3bn are obtained according
to the user key in which the binary sequence 0 and 1 denote distribution mode of
processors and the number of binary sequence n denotes cycle number Then the
pseudorandom 2D matrix is generated by 2D-Logistic map and adaptive segmentation is
applied in image matrix and pseudorandom matrix according to the value and the number
of binary sequence The parallel operation is used among blocks to improve efficiency
and meet real-time demand in transmission processes However the pixel permutation is
applied in partitioned matrix through ergodic matrix generated by pseudo-random matrix-
block to decrease the correlation of adjacent pixels Then the pixel substitution is used for
fully diffusing through cipher block chaining mode until n cycles The proposed
algorithm can meet the three requirements of parallel operation in image encryption and
the real-time requirement in transmission processes The security is proved by theoretical
analysis and simulation results
Advantages
1Security is provided
2Effeciency is improved
8
3) LJani Anbarasi GSAnandha Mala Modigari Narendra (2014) ldquoDNA
based Multi-Secret Image Sharingrdquo International Conference on
Information and Communication Technologies (ICICT)
Multiple secret sharing algorithm using the YCH scheme combined with
DNA encoding is proposed focusing at better security Firstly DNA encoding for
multiple images is carried out then the addition of these encoded components by DNA is
performed Secondly the (t n) scheme used the Lagrange interpolation polynomial to
share these DNA scrambled matrices is performed bdquot‟ denotes threshold value and bdquon‟
denotes the number of participants The resulting scrambled images are encrypted into n
shares using Shamir‟s polynomial Thirdly these shares are embedded using a modular
operation Finally bdquot‟ or more shares are pooled which reconstructs the scrambled
matrices and by decoding the DNA scrambled matrices multiple secrets are
reconstructed without loss The simulation results and the security analysis prove that this
algorithm is perfect and produces results with better PSNR value The correlation co-
efficient shows that this also has the ability of resisting various attacks
Advantages
1Security is better
2Resistance against Attack
9
4)Della Babya Jitha Thomasa Gisny Augustinea Elsa Georgea Neenu
Rosia Michaela (2014) ldquoA Novel DWT based Image Securing Method using
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Steganography is a data hiding technique that is widely used in various
information securing applications Steganography transmits data by hiding the existence
of the message so that a viewer cannot identify the transmission of message and hence
not able to decrypt it This work proposes a data securing technique that is used for
hiding multiple color images into a single color image using the Discrete Wavelet
Transform The cover image is split up into R G and B planes Secret images are
embedded into these planes An N-level decomposition of the cover image and the secret
images are done and some frequency components of the same are combined Secret
images are then extracted from the stego image Here the stego image obtained has a less
perceptible changes compared to the original image with high overall security
Advantages
1Less perceptible changes
2Overall security is high
10
5)Prasenjit Dasa Subhrajyoti Deba Nirmalya Kara Baby
Bhattacharyaa(2014)ldquoAn Improved DNA based Dual Cover
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Dual cover steganography is an evolving technique in the field of covert
data transmission This paper focuses on the concept of using a theoretical single stranded
DNA (ssDNA) as a primary cover which is extracted from an inconspicuous cover
image They have analyzed the security loopholes and performance issues of the existing
algorithm and proposed an improved algorithm on the same basisthey use 2D Logistic
map for encrypting the cover imageThen overall encryption is RC43 types of encryption
is generally used Performance of both the algorithms are tested against several visual
and statistical attacks and parameterized in terms of both security and capacity The
comparison shows that the proposed improvements provide better overall security
Advantages
1 Robustness against various attack
2 Performance measure are calculated
3 Data hiding improves security
11
6) Bhavana Agrawal Himani Agrawal ( May-2013) ldquoImplementation of AES
and RSA Using Chaos systemrdquo International Journal of Scientific amp
Engineering Research Vol 4 No 5
This paper presents two cryptographic algorithm AES and RSA Using Chaos
Chaos has attracted much attention in the field of cryptography It describes a system
which is sensitive to initial condition It generates apparently random behavior but at the
same time is completely deterministic Chaos function is used to increase the complexity
and Security of the SystemAES and RSA are the two cryptographic algorithms In AES
we apply the Chaos on S-box where as in RSA we mix the plaintext with Chaos sequence
First then apply for encryption and decryption After Implementing AES and RSA they
compare both the technique on the basis of speed
Advantages
1Chaos function is used to improve complexity
2The speed has been improved with combined technique of AES and RSA along with
chaos technique
12
7) Pahrul Irfan Yudi Prayudi Imam Riadi ( August 2015) ldquo Image
Encryption using Combination of Chaotic System and Rivers Shamir
Adleman (RSA)rdquo International Journal of Computer Applications Vol 123
No6
Security and confidentiality of data or information at the present time has
become an important concern Advanced methods for secure transmission storage and
retrieval of digital images are increasingly needed for a number of military medical
homeland security and other applications Various kinds of techniques for increase
security data or information already is developed one common way is by cryptographic
techniques Cryptography is science to maintain the security of the message by changing
data or information into a different form so the message cannot be recognized To
compensate for increasing computing speeds increases it takes more than one encryption
algorithm to improve security of digital images One way is by using algorithms to
double cryptography do encryption and decryption Cryptographic algorithm often used
today and the proven strength specially the digital image is Algorithm with Chaos
system To improve security at the image then we use Additional algorithms namely
Rivers algorithm Shamir Adleman (RSA) which known as the standard of cryptography
algorithms This research aims to optimize security bitmap image format by combining
the two algorithms namely Chaos-based algorithms and RSA algorithm into one
application Experiments conducted show that the proposed algorithm possesses robust
security features such as fairly uniform distribution high sensitivity to both keys and
plain images almost ideal entropy and the ability to highly de-correlate adjacent pixels
in the cipher images Furthermore it has a large key space and transform image to pure
text file which greatly increases its security for image encryption
Advantages
1 It aims to optimize security bitmap image format by combining the two algorithms
namely Chaos-based algorithms and RSA algorithm into one application
13
8) Weiming Zhang KedeMa NenghaiYu(2013) ldquo Reversibility improved
data hiding in encrypted imagesrdquo School of Information Science and
Technology
A novel reversible data hiding technique in encrypted images is presented in this
paper Instead of embedding data in encrypted images directly some pixels are estimated
before encryption so that additional data can be embedded in the estimating errors A
bench mark encryption algorithm (eg AES) is applied to the rest pixels of the image and
a special encryption scheme is designed to encrypt the estimating errors Without the
encryption key one cannot get access to the original image However provided with the
data hiding key only he can embed in or extract from the encrypted image additional data
without knowledge about the original image Moreover the data extraction and image
recovery are free of errors for all images Experiments demonstrate the feasibility and
efficiency of the proposed method especially in aspect of embedding rate versus Peak
Signal-to-Noise Ratio (PSNR)
The paper proposes a novel method to significantly improve the performance by
reversing the order of encryption and vacating room In the light of this idea we empty
out room prior to image encryption by shifting the histogram of estimating errors of some
pixels and the emptied out room will be used for data hiding The proposed method is
composed of four primary steps vacating room and encrypting image data hiding in the
encrypted image data extraction and image recovery Two different schemes extraction
before decryption and decryption before extraction are raised to cope with different
applications
Advantages
(i) Achieves excellent performance in three aspects complete reversibility PSNR
under given embedding rate separability between data higher extraction and
image decryption
14
CHAPTER 3
PROPOSED METHODOLOGY
The proposed data hiding scheme aims at the security of the hidden data
Embedding is performed in spatial domain The data to be embedded is converted into
binary form from ASCII code using chaos encryption and is embedded into the cover
image obtained after 2D logistic map This embedded image is secured using symmetric
key (K1)They are converted into DNA sequence to provide additional level of security
The hidden data can be extracted from the cover image accurately with the help of
decryption techniques and secret key (K1) The cover image can also be extracted
without any distortion The fig 31 shows the workflow
Fig 31 Work Flow Diagram
SECRET DATA
COVER IMAGE
CHAOTIC
ENCRYPTION
ENCRY 2D LOGISTIC
ENCRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
CHAOTIC
DECRYPTION
ENCRY
SECRET DATA
COVER IMAGE 2D LOGISTIC
DECRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
15
31 Chaotic Encryption
Chaotic cryptography is the application of the mathematical chaos theory to the
practice of the cryptography the study or techniques used to privately and securely
transmit information with the presence of an third-party or adversary The use of chaos
or randomness in cryptography has long been sought after by entities wanting a new way
to encrypt messages However because of the lack of thorough provable security
properties and low acceptable performance chaotic cryptography has encountered
setbacksIn order to use chaos theory acceptably in cryptography they must first be
mapped to each other Properties in chaotic systems and cryptographic primitives share
unique characteristics that allow for the chaotic systems to be applied to cryptography If
chaotic parameters as well as cryptographic keys can be mapped symmetrically or
mapped to produce acceptable and functional outputs it will make it next to impossible
for an adversary to find the outputs without any knowledge the initial values Since
chaotic maps in a real life scenario require a set of numbers that are limited they may in
fact have no real purpose in a cryptosystem if the chaotic behavior can be predicted To
counter this possibility there exists simple to advanced ciphers Chaos theory used in
cryptosystems for commercial implementation has proven to be unsuccessful mainly
because a chaos theories‟ requirement to use intervals of real numbers Given enough
resources and time an adversary could be able to predict functional outcomes Since
chaotic cryptosystems have no root in number theory this would make it difficult or
impossible to implement therefore impractical
32 The RSA Algorithm
The RSA cryptosystem named after its inventors R Rivest A Shamir and L
Adleman is the most widely used public key Cryptosystem It may be used to provide
both secrecy and digital signatures and its security is based on the intractability of the
integer factorizationThe RSA algorithm involves three steps key generation encryption
and decryption
16
321 Key Generation
RSA involves a public key and a private key The public key can be known to
everyone and is used for encrypting messages Messages encrypted with the public key
can only be decrypted in a reasonable amount of time using the private key The keys for
the RSA algorithm are generated the following way To generate the two keys choose
two random large prime numbers p and q For maximum security choose p and q of
equal length Then randomly choose the encryption key e such that e and ( p minus1) (q minus1)
are relatively prime Finally use the extended Euclidean algorithm to compute the
decryption key d such that
d= e-1
mod ( (p-1) (q-1))
Note that d and n are also relatively prime The numbers e and K are the public
key the number d is the private key The two primes p and q are no longer needed They
should be discarded but never revealed
322 Encryption
Firstly receiver transmits her public key (n e) to sender and keeps the private key
secret If sender wishes to send message M to receiver Sender change the message M in
to integer m such that 0 le mltn Then sender computes the cipher text c corresponding to
Cequiv me
(mod n)
323 Decryption
Receiver can recover M from c by using private key exponent d via computing
M equiv cd
(mod n)
17
Algorithm
1Select any two prime numbers say (pq)
2Compute n=pq and also compute empty(119899)=(p-1)(q-1)
3Choose e such that 1ltelt empty(119899)
4Choose d such that (de)mod empty(119899)=1
5Public key is (en) and Private key is (dn)
6 If egt=2 then check i==1 if so return 1 else return 0
7In a iteration check for e(i)==1 if so take mod function of message with n
8Message is converted to cipher text in ASCII form with the key generated
9The cipher data in ASCII form is converted to binary form
33 2D Logistic Encryption
The chaotic system is a deterministic nonlinear system It possesses a varied
characteristics such as high sensitivity to initial conditions and system parameters
random-like behaviors and so forth Chaotic sequences produced by chaotic maps are
pseudo-random sequences their structures are very complex and difficult to be analyzed
and predicted In other words chaotic systems can improve the security of encryption
systems Thus it is advisable to encrypt digital image with chaotic systems There are
two chaotic maps one is logistic map and the other is 2D logistic map In the proposed
work 2D logistic map is used
Logistic map is an example for chaotic map and it is described as follows
x(n+1)=μ x(n)(1minusx(n))
μ is a positive constant sometimes known as the biotic potential gives the so-called
logistic map x(n) is series of values used to plot Logistic mapwhere μ isin [04] x(n) isin
(01) and n = 01 2hellip The research result shows that the system is in chaotic state
under the condition that 356994 lt μ le 4
18
2D logistic map is described in as follows
119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2
119910119894+1 = 1205832 lowast 119910119894(1 minus 119910119894) + 1205732((119909119894)2 + 119909119894 lowast 119910119894)
Where z(xy) is the logistic map 1205831 and 1205832 are positive constant sometimes known as the
biotic potential gives the so-called logistic map x be the position of images in x axis
and y be the position of images in y axis 1205731 and 1205732 are the correlation constantsWhen
275 lt μ1 le 34 275 lt μ2 le 345 015 lt 1205731le 021 and 013 lt 1205732 le 015 the system is in
chaotic state and can generate two chaotic sequences in the region (01] Due to the
system parameter γ1 and γ2 which have smaller value range we set γ1 = 017 and γ2 =
014 other parameters can be seen as secret keys
Algorithm
1A random key is generated in binary form ( 1times256) and it is stored in a array
2The random key generated is translated to map format using block processing (4times4)
3 The row and column wise transformation is carried out
4The key is now used to encrypt the cover image
52D logistic image undergoes substitution and permutation (column and row wise
shuffling is done)
34 DNA Sequence
A single DNA sequence is made up of four nucleic acid
bases A (adenine) C (cytosine) G (guanine) and T (thymine) where A and T are
complements and C and G are complements Let binary number 0 and 1 be
complements so 00 and 11 are complements and 01 and 10 are complements Thus we
can use these four bases A T G and C to encode 01 10 00 and 11 respectively The
encoding method still satisfies the Watson-Crick complement rule Usually each pixel
value of the 8 bit grey image can be expressed to 8 bits binary stream The binary stream
can be encoded to a DNA sequence whose length is 4 For example if the first pixel
19
value of the original image is 75 convert it into a binary stream [01001011] By using the
above DNA encoding rule to encode the stream we can get a DNA sequence [AGTC]
whereas we use A T G and C to express 01 10 00 and 11 respectively We can get a
binary sequence [01001011]
35 Attacks
The steganographic algorithm is used to embed secret messages into cover
image To obtain stego image while exchanging these stego-image through the public
communication channel various attacks have been made The are generally classified
into two types intentional or unintentional attacks Examples of unintentional attacks are
transmission errors lossy compression and changing the visual properties of the stego
document Intentional attacks on the other hand are deliberate attempts to distinguish
stego-objects from unmodified objects and thus detect the presence of covert
communication Attack methods generally exploit the fact that embedding information
usually changes the statistical properties of the objects compared to typical unmodified
objects In this proposed algorithm various attacks have been applied on the encrypted
image They are as follows
1)Shearing
2) Image Scaling
3) Image Rotating
4) Image color reduction
5) Image blurred
6) Image flip
7) cropping and intensity transformation
8) Image sharpening
9) Gaussian Noise and filtering
10) Image Contrast
11) Speckle Noise and Filtering
20
351 Shearing
The image is resized with the scale factor Resized image is rotated with
angle(theta)Finally spatial transformation from control point pairs is implemented
For example
Scale factor 09
Theta10
Fig 32 Shearing Image
352 Image Scaling
It resizes the image with a scale factor and rotation is performed It rotates the
image by angle (degrees) in a counterclockwise direction around its center point To
rotate the image clockwise specify a negative value for angle It makes the output image
large enough to contain the entire rotated image It uses nearest neighbour interpolation
setting the values of pixels in Output image that are outside the rotated image to 0 (zero)
For example
Scale Factor07
Theta30
scaling Image
21
Fig 33 Scaling Image
353 Rotation
It rotates the image by angle degrees in a counterclockwise direction around its
center point To rotate the image clockwise specify a negative value for angle It makes
the output image large enough to contain the entire rotated image It uses nearest
neighbour interpolation setting the values of pixels in Output image that are outside the
rotated image to 0 (zero)
For Example
Theta180
Fig 34 Rotation Image
354 Colour Reduced Image
It creates an indexed image approximation of the RGB image in the array RGB by
dithering the colors in the colormap map The colormap cannot have more than 65536
resized and rotated image
Rotated image
22
colors
For Example
Indexed image with 32 Colors
Fig 35 Colour Reduced Image
355 Blur Image
The image is blurred by using N-D filtering of multidimensional images It filters
the multidimensional array of original image with the multidimensional filter The array
of original image can be logical or a nonsparse numeric array of any class and dimension
The result image has the same size and class as of original image
Fig 36 Blur Image
Color reduced image
50 100 150 200 250 300 350 400 450 500
50
100
150
200
250
300
350
400
450
500
Blurred image
23
356 Flipped Image
It flips the image upside down Flipping is used to invert the image
Fig 37 Flipped Image
357 Cropped Image
It creates an interactive crop image tool associated with the image displayed in the
current figure called the target image The crop image tool is a movable resizable
rectangle that you can position interactively using the mouse When the crop image tool
is active the pointer changes to cross hairs when it is moved over the target image
Using the mouse image to be cropped can be specified by clicking and dragging the
mouse The crop rectangle using the mouse can be moved or resized When sizing and
positioning of the crop rectangle is finished create the cropped image by double-clicking
the left mouse button or by choosing crop image from the context menu Image cropping
returns the cropped image
Flipped image
24
Fig 38 Cropped Image
358 Intensity Transformation Adjust
It maps the intensity values in grayscale image to new values in resultant image
such that 1 of data is saturated at low and high intensity of original image This
increases the contrast of the output image
Fig 39 Intensity Transformation Image
Cropped Image
Intensity Transformation
25
359 Sharpening
Input array values outside the bounds of the array are assumed to equal the nearest
array border value The image is sharpened by using N-D filtering of multidimensional
images It filters the multidimensional array of original image with the multidimensional
filter The array of original image can be logical or a nonsparse numeric array of any
class and dimension The result image has the same size and class as of original image
Fig 310 Sharpened Image
3510 Gaussian Noise and Median Filtering
It adds Gaussian noise to the images Gaussian white noise have constant mean
and variance The noise added image is filtered using Median Filtering Median filtering
is a nonlinear operation often used in image processing to reduce salt and pepper noise
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Sharpened Image
26
Fig 311 Gaussian Noise and Median Filter Image
3511 Histogram of contrast image
It enhances the contrast of images by transforming the values in an intensity
image or the values in the colormap of an indexed image so that the histogram of the
output image approximately matches a specified histogram
Fig 312 Contrast Image
Gaussian Noise
Median Filtering
Contrast Image
27
Fig 313 Histogram of Contrast Image
3512 Speckle noise and Median Filtering
It adds multiplicative noise to the image I using the equation J = I+nI where n is
uniformly distributed random noise with mean 0 and variance v The default for v is 004
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Fig 314 Speckle Noise and Median Filter Image
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Histogram of Contrast Image
0 50 100 150 200 250
Speckle Noise
Median Filtering
28
36 Proposed Algorithm
Step1 Enter two keys a private key and a public key through which the RSA algorithm is
performed
Step2These key are used for encrypting the secret data using chaotic algorithm with
threshold of 2
Step3The secret data is converted into binary format from ASCII code
Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied
Step5The encryption is carried out with the key generated randomly in binary(1times256)
Step6 The 2D logistic substitution and permutation are carried out
Step7The resulting binary sequence is added with the encrypted text in LSB
Step8The image is converted to DNA sequence and transmitted
Step9 Various Attacks have been applied on the resultant image
Step10The inverse process is carried out to retrieve the original image and data
Step11The Performance Metrics have been calculated
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
7
2) Jing Wang Guoping Jiang (2013) ldquoA Self-Adaptive Parallel Encryption
Algorithm Based on Discrete 2D-Logistic Maprdquo International Journal of
Modern Nonlinear Theory and Application
A self-adaptive parallel encryption algorithm based on discrete 2D-Logistic
map is developed according to the position scrambling and diffusion of multi-direction in
variable space of spatial chaos The binary sequences b1b2b3bn are obtained according
to the user key in which the binary sequence 0 and 1 denote distribution mode of
processors and the number of binary sequence n denotes cycle number Then the
pseudorandom 2D matrix is generated by 2D-Logistic map and adaptive segmentation is
applied in image matrix and pseudorandom matrix according to the value and the number
of binary sequence The parallel operation is used among blocks to improve efficiency
and meet real-time demand in transmission processes However the pixel permutation is
applied in partitioned matrix through ergodic matrix generated by pseudo-random matrix-
block to decrease the correlation of adjacent pixels Then the pixel substitution is used for
fully diffusing through cipher block chaining mode until n cycles The proposed
algorithm can meet the three requirements of parallel operation in image encryption and
the real-time requirement in transmission processes The security is proved by theoretical
analysis and simulation results
Advantages
1Security is provided
2Effeciency is improved
8
3) LJani Anbarasi GSAnandha Mala Modigari Narendra (2014) ldquoDNA
based Multi-Secret Image Sharingrdquo International Conference on
Information and Communication Technologies (ICICT)
Multiple secret sharing algorithm using the YCH scheme combined with
DNA encoding is proposed focusing at better security Firstly DNA encoding for
multiple images is carried out then the addition of these encoded components by DNA is
performed Secondly the (t n) scheme used the Lagrange interpolation polynomial to
share these DNA scrambled matrices is performed bdquot‟ denotes threshold value and bdquon‟
denotes the number of participants The resulting scrambled images are encrypted into n
shares using Shamir‟s polynomial Thirdly these shares are embedded using a modular
operation Finally bdquot‟ or more shares are pooled which reconstructs the scrambled
matrices and by decoding the DNA scrambled matrices multiple secrets are
reconstructed without loss The simulation results and the security analysis prove that this
algorithm is perfect and produces results with better PSNR value The correlation co-
efficient shows that this also has the ability of resisting various attacks
Advantages
1Security is better
2Resistance against Attack
9
4)Della Babya Jitha Thomasa Gisny Augustinea Elsa Georgea Neenu
Rosia Michaela (2014) ldquoA Novel DWT based Image Securing Method using
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Steganography is a data hiding technique that is widely used in various
information securing applications Steganography transmits data by hiding the existence
of the message so that a viewer cannot identify the transmission of message and hence
not able to decrypt it This work proposes a data securing technique that is used for
hiding multiple color images into a single color image using the Discrete Wavelet
Transform The cover image is split up into R G and B planes Secret images are
embedded into these planes An N-level decomposition of the cover image and the secret
images are done and some frequency components of the same are combined Secret
images are then extracted from the stego image Here the stego image obtained has a less
perceptible changes compared to the original image with high overall security
Advantages
1Less perceptible changes
2Overall security is high
10
5)Prasenjit Dasa Subhrajyoti Deba Nirmalya Kara Baby
Bhattacharyaa(2014)ldquoAn Improved DNA based Dual Cover
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Dual cover steganography is an evolving technique in the field of covert
data transmission This paper focuses on the concept of using a theoretical single stranded
DNA (ssDNA) as a primary cover which is extracted from an inconspicuous cover
image They have analyzed the security loopholes and performance issues of the existing
algorithm and proposed an improved algorithm on the same basisthey use 2D Logistic
map for encrypting the cover imageThen overall encryption is RC43 types of encryption
is generally used Performance of both the algorithms are tested against several visual
and statistical attacks and parameterized in terms of both security and capacity The
comparison shows that the proposed improvements provide better overall security
Advantages
1 Robustness against various attack
2 Performance measure are calculated
3 Data hiding improves security
11
6) Bhavana Agrawal Himani Agrawal ( May-2013) ldquoImplementation of AES
and RSA Using Chaos systemrdquo International Journal of Scientific amp
Engineering Research Vol 4 No 5
This paper presents two cryptographic algorithm AES and RSA Using Chaos
Chaos has attracted much attention in the field of cryptography It describes a system
which is sensitive to initial condition It generates apparently random behavior but at the
same time is completely deterministic Chaos function is used to increase the complexity
and Security of the SystemAES and RSA are the two cryptographic algorithms In AES
we apply the Chaos on S-box where as in RSA we mix the plaintext with Chaos sequence
First then apply for encryption and decryption After Implementing AES and RSA they
compare both the technique on the basis of speed
Advantages
1Chaos function is used to improve complexity
2The speed has been improved with combined technique of AES and RSA along with
chaos technique
12
7) Pahrul Irfan Yudi Prayudi Imam Riadi ( August 2015) ldquo Image
Encryption using Combination of Chaotic System and Rivers Shamir
Adleman (RSA)rdquo International Journal of Computer Applications Vol 123
No6
Security and confidentiality of data or information at the present time has
become an important concern Advanced methods for secure transmission storage and
retrieval of digital images are increasingly needed for a number of military medical
homeland security and other applications Various kinds of techniques for increase
security data or information already is developed one common way is by cryptographic
techniques Cryptography is science to maintain the security of the message by changing
data or information into a different form so the message cannot be recognized To
compensate for increasing computing speeds increases it takes more than one encryption
algorithm to improve security of digital images One way is by using algorithms to
double cryptography do encryption and decryption Cryptographic algorithm often used
today and the proven strength specially the digital image is Algorithm with Chaos
system To improve security at the image then we use Additional algorithms namely
Rivers algorithm Shamir Adleman (RSA) which known as the standard of cryptography
algorithms This research aims to optimize security bitmap image format by combining
the two algorithms namely Chaos-based algorithms and RSA algorithm into one
application Experiments conducted show that the proposed algorithm possesses robust
security features such as fairly uniform distribution high sensitivity to both keys and
plain images almost ideal entropy and the ability to highly de-correlate adjacent pixels
in the cipher images Furthermore it has a large key space and transform image to pure
text file which greatly increases its security for image encryption
Advantages
1 It aims to optimize security bitmap image format by combining the two algorithms
namely Chaos-based algorithms and RSA algorithm into one application
13
8) Weiming Zhang KedeMa NenghaiYu(2013) ldquo Reversibility improved
data hiding in encrypted imagesrdquo School of Information Science and
Technology
A novel reversible data hiding technique in encrypted images is presented in this
paper Instead of embedding data in encrypted images directly some pixels are estimated
before encryption so that additional data can be embedded in the estimating errors A
bench mark encryption algorithm (eg AES) is applied to the rest pixels of the image and
a special encryption scheme is designed to encrypt the estimating errors Without the
encryption key one cannot get access to the original image However provided with the
data hiding key only he can embed in or extract from the encrypted image additional data
without knowledge about the original image Moreover the data extraction and image
recovery are free of errors for all images Experiments demonstrate the feasibility and
efficiency of the proposed method especially in aspect of embedding rate versus Peak
Signal-to-Noise Ratio (PSNR)
The paper proposes a novel method to significantly improve the performance by
reversing the order of encryption and vacating room In the light of this idea we empty
out room prior to image encryption by shifting the histogram of estimating errors of some
pixels and the emptied out room will be used for data hiding The proposed method is
composed of four primary steps vacating room and encrypting image data hiding in the
encrypted image data extraction and image recovery Two different schemes extraction
before decryption and decryption before extraction are raised to cope with different
applications
Advantages
(i) Achieves excellent performance in three aspects complete reversibility PSNR
under given embedding rate separability between data higher extraction and
image decryption
14
CHAPTER 3
PROPOSED METHODOLOGY
The proposed data hiding scheme aims at the security of the hidden data
Embedding is performed in spatial domain The data to be embedded is converted into
binary form from ASCII code using chaos encryption and is embedded into the cover
image obtained after 2D logistic map This embedded image is secured using symmetric
key (K1)They are converted into DNA sequence to provide additional level of security
The hidden data can be extracted from the cover image accurately with the help of
decryption techniques and secret key (K1) The cover image can also be extracted
without any distortion The fig 31 shows the workflow
Fig 31 Work Flow Diagram
SECRET DATA
COVER IMAGE
CHAOTIC
ENCRYPTION
ENCRY 2D LOGISTIC
ENCRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
CHAOTIC
DECRYPTION
ENCRY
SECRET DATA
COVER IMAGE 2D LOGISTIC
DECRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
15
31 Chaotic Encryption
Chaotic cryptography is the application of the mathematical chaos theory to the
practice of the cryptography the study or techniques used to privately and securely
transmit information with the presence of an third-party or adversary The use of chaos
or randomness in cryptography has long been sought after by entities wanting a new way
to encrypt messages However because of the lack of thorough provable security
properties and low acceptable performance chaotic cryptography has encountered
setbacksIn order to use chaos theory acceptably in cryptography they must first be
mapped to each other Properties in chaotic systems and cryptographic primitives share
unique characteristics that allow for the chaotic systems to be applied to cryptography If
chaotic parameters as well as cryptographic keys can be mapped symmetrically or
mapped to produce acceptable and functional outputs it will make it next to impossible
for an adversary to find the outputs without any knowledge the initial values Since
chaotic maps in a real life scenario require a set of numbers that are limited they may in
fact have no real purpose in a cryptosystem if the chaotic behavior can be predicted To
counter this possibility there exists simple to advanced ciphers Chaos theory used in
cryptosystems for commercial implementation has proven to be unsuccessful mainly
because a chaos theories‟ requirement to use intervals of real numbers Given enough
resources and time an adversary could be able to predict functional outcomes Since
chaotic cryptosystems have no root in number theory this would make it difficult or
impossible to implement therefore impractical
32 The RSA Algorithm
The RSA cryptosystem named after its inventors R Rivest A Shamir and L
Adleman is the most widely used public key Cryptosystem It may be used to provide
both secrecy and digital signatures and its security is based on the intractability of the
integer factorizationThe RSA algorithm involves three steps key generation encryption
and decryption
16
321 Key Generation
RSA involves a public key and a private key The public key can be known to
everyone and is used for encrypting messages Messages encrypted with the public key
can only be decrypted in a reasonable amount of time using the private key The keys for
the RSA algorithm are generated the following way To generate the two keys choose
two random large prime numbers p and q For maximum security choose p and q of
equal length Then randomly choose the encryption key e such that e and ( p minus1) (q minus1)
are relatively prime Finally use the extended Euclidean algorithm to compute the
decryption key d such that
d= e-1
mod ( (p-1) (q-1))
Note that d and n are also relatively prime The numbers e and K are the public
key the number d is the private key The two primes p and q are no longer needed They
should be discarded but never revealed
322 Encryption
Firstly receiver transmits her public key (n e) to sender and keeps the private key
secret If sender wishes to send message M to receiver Sender change the message M in
to integer m such that 0 le mltn Then sender computes the cipher text c corresponding to
Cequiv me
(mod n)
323 Decryption
Receiver can recover M from c by using private key exponent d via computing
M equiv cd
(mod n)
17
Algorithm
1Select any two prime numbers say (pq)
2Compute n=pq and also compute empty(119899)=(p-1)(q-1)
3Choose e such that 1ltelt empty(119899)
4Choose d such that (de)mod empty(119899)=1
5Public key is (en) and Private key is (dn)
6 If egt=2 then check i==1 if so return 1 else return 0
7In a iteration check for e(i)==1 if so take mod function of message with n
8Message is converted to cipher text in ASCII form with the key generated
9The cipher data in ASCII form is converted to binary form
33 2D Logistic Encryption
The chaotic system is a deterministic nonlinear system It possesses a varied
characteristics such as high sensitivity to initial conditions and system parameters
random-like behaviors and so forth Chaotic sequences produced by chaotic maps are
pseudo-random sequences their structures are very complex and difficult to be analyzed
and predicted In other words chaotic systems can improve the security of encryption
systems Thus it is advisable to encrypt digital image with chaotic systems There are
two chaotic maps one is logistic map and the other is 2D logistic map In the proposed
work 2D logistic map is used
Logistic map is an example for chaotic map and it is described as follows
x(n+1)=μ x(n)(1minusx(n))
μ is a positive constant sometimes known as the biotic potential gives the so-called
logistic map x(n) is series of values used to plot Logistic mapwhere μ isin [04] x(n) isin
(01) and n = 01 2hellip The research result shows that the system is in chaotic state
under the condition that 356994 lt μ le 4
18
2D logistic map is described in as follows
119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2
119910119894+1 = 1205832 lowast 119910119894(1 minus 119910119894) + 1205732((119909119894)2 + 119909119894 lowast 119910119894)
Where z(xy) is the logistic map 1205831 and 1205832 are positive constant sometimes known as the
biotic potential gives the so-called logistic map x be the position of images in x axis
and y be the position of images in y axis 1205731 and 1205732 are the correlation constantsWhen
275 lt μ1 le 34 275 lt μ2 le 345 015 lt 1205731le 021 and 013 lt 1205732 le 015 the system is in
chaotic state and can generate two chaotic sequences in the region (01] Due to the
system parameter γ1 and γ2 which have smaller value range we set γ1 = 017 and γ2 =
014 other parameters can be seen as secret keys
Algorithm
1A random key is generated in binary form ( 1times256) and it is stored in a array
2The random key generated is translated to map format using block processing (4times4)
3 The row and column wise transformation is carried out
4The key is now used to encrypt the cover image
52D logistic image undergoes substitution and permutation (column and row wise
shuffling is done)
34 DNA Sequence
A single DNA sequence is made up of four nucleic acid
bases A (adenine) C (cytosine) G (guanine) and T (thymine) where A and T are
complements and C and G are complements Let binary number 0 and 1 be
complements so 00 and 11 are complements and 01 and 10 are complements Thus we
can use these four bases A T G and C to encode 01 10 00 and 11 respectively The
encoding method still satisfies the Watson-Crick complement rule Usually each pixel
value of the 8 bit grey image can be expressed to 8 bits binary stream The binary stream
can be encoded to a DNA sequence whose length is 4 For example if the first pixel
19
value of the original image is 75 convert it into a binary stream [01001011] By using the
above DNA encoding rule to encode the stream we can get a DNA sequence [AGTC]
whereas we use A T G and C to express 01 10 00 and 11 respectively We can get a
binary sequence [01001011]
35 Attacks
The steganographic algorithm is used to embed secret messages into cover
image To obtain stego image while exchanging these stego-image through the public
communication channel various attacks have been made The are generally classified
into two types intentional or unintentional attacks Examples of unintentional attacks are
transmission errors lossy compression and changing the visual properties of the stego
document Intentional attacks on the other hand are deliberate attempts to distinguish
stego-objects from unmodified objects and thus detect the presence of covert
communication Attack methods generally exploit the fact that embedding information
usually changes the statistical properties of the objects compared to typical unmodified
objects In this proposed algorithm various attacks have been applied on the encrypted
image They are as follows
1)Shearing
2) Image Scaling
3) Image Rotating
4) Image color reduction
5) Image blurred
6) Image flip
7) cropping and intensity transformation
8) Image sharpening
9) Gaussian Noise and filtering
10) Image Contrast
11) Speckle Noise and Filtering
20
351 Shearing
The image is resized with the scale factor Resized image is rotated with
angle(theta)Finally spatial transformation from control point pairs is implemented
For example
Scale factor 09
Theta10
Fig 32 Shearing Image
352 Image Scaling
It resizes the image with a scale factor and rotation is performed It rotates the
image by angle (degrees) in a counterclockwise direction around its center point To
rotate the image clockwise specify a negative value for angle It makes the output image
large enough to contain the entire rotated image It uses nearest neighbour interpolation
setting the values of pixels in Output image that are outside the rotated image to 0 (zero)
For example
Scale Factor07
Theta30
scaling Image
21
Fig 33 Scaling Image
353 Rotation
It rotates the image by angle degrees in a counterclockwise direction around its
center point To rotate the image clockwise specify a negative value for angle It makes
the output image large enough to contain the entire rotated image It uses nearest
neighbour interpolation setting the values of pixels in Output image that are outside the
rotated image to 0 (zero)
For Example
Theta180
Fig 34 Rotation Image
354 Colour Reduced Image
It creates an indexed image approximation of the RGB image in the array RGB by
dithering the colors in the colormap map The colormap cannot have more than 65536
resized and rotated image
Rotated image
22
colors
For Example
Indexed image with 32 Colors
Fig 35 Colour Reduced Image
355 Blur Image
The image is blurred by using N-D filtering of multidimensional images It filters
the multidimensional array of original image with the multidimensional filter The array
of original image can be logical or a nonsparse numeric array of any class and dimension
The result image has the same size and class as of original image
Fig 36 Blur Image
Color reduced image
50 100 150 200 250 300 350 400 450 500
50
100
150
200
250
300
350
400
450
500
Blurred image
23
356 Flipped Image
It flips the image upside down Flipping is used to invert the image
Fig 37 Flipped Image
357 Cropped Image
It creates an interactive crop image tool associated with the image displayed in the
current figure called the target image The crop image tool is a movable resizable
rectangle that you can position interactively using the mouse When the crop image tool
is active the pointer changes to cross hairs when it is moved over the target image
Using the mouse image to be cropped can be specified by clicking and dragging the
mouse The crop rectangle using the mouse can be moved or resized When sizing and
positioning of the crop rectangle is finished create the cropped image by double-clicking
the left mouse button or by choosing crop image from the context menu Image cropping
returns the cropped image
Flipped image
24
Fig 38 Cropped Image
358 Intensity Transformation Adjust
It maps the intensity values in grayscale image to new values in resultant image
such that 1 of data is saturated at low and high intensity of original image This
increases the contrast of the output image
Fig 39 Intensity Transformation Image
Cropped Image
Intensity Transformation
25
359 Sharpening
Input array values outside the bounds of the array are assumed to equal the nearest
array border value The image is sharpened by using N-D filtering of multidimensional
images It filters the multidimensional array of original image with the multidimensional
filter The array of original image can be logical or a nonsparse numeric array of any
class and dimension The result image has the same size and class as of original image
Fig 310 Sharpened Image
3510 Gaussian Noise and Median Filtering
It adds Gaussian noise to the images Gaussian white noise have constant mean
and variance The noise added image is filtered using Median Filtering Median filtering
is a nonlinear operation often used in image processing to reduce salt and pepper noise
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Sharpened Image
26
Fig 311 Gaussian Noise and Median Filter Image
3511 Histogram of contrast image
It enhances the contrast of images by transforming the values in an intensity
image or the values in the colormap of an indexed image so that the histogram of the
output image approximately matches a specified histogram
Fig 312 Contrast Image
Gaussian Noise
Median Filtering
Contrast Image
27
Fig 313 Histogram of Contrast Image
3512 Speckle noise and Median Filtering
It adds multiplicative noise to the image I using the equation J = I+nI where n is
uniformly distributed random noise with mean 0 and variance v The default for v is 004
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Fig 314 Speckle Noise and Median Filter Image
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Histogram of Contrast Image
0 50 100 150 200 250
Speckle Noise
Median Filtering
28
36 Proposed Algorithm
Step1 Enter two keys a private key and a public key through which the RSA algorithm is
performed
Step2These key are used for encrypting the secret data using chaotic algorithm with
threshold of 2
Step3The secret data is converted into binary format from ASCII code
Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied
Step5The encryption is carried out with the key generated randomly in binary(1times256)
Step6 The 2D logistic substitution and permutation are carried out
Step7The resulting binary sequence is added with the encrypted text in LSB
Step8The image is converted to DNA sequence and transmitted
Step9 Various Attacks have been applied on the resultant image
Step10The inverse process is carried out to retrieve the original image and data
Step11The Performance Metrics have been calculated
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
8
3) LJani Anbarasi GSAnandha Mala Modigari Narendra (2014) ldquoDNA
based Multi-Secret Image Sharingrdquo International Conference on
Information and Communication Technologies (ICICT)
Multiple secret sharing algorithm using the YCH scheme combined with
DNA encoding is proposed focusing at better security Firstly DNA encoding for
multiple images is carried out then the addition of these encoded components by DNA is
performed Secondly the (t n) scheme used the Lagrange interpolation polynomial to
share these DNA scrambled matrices is performed bdquot‟ denotes threshold value and bdquon‟
denotes the number of participants The resulting scrambled images are encrypted into n
shares using Shamir‟s polynomial Thirdly these shares are embedded using a modular
operation Finally bdquot‟ or more shares are pooled which reconstructs the scrambled
matrices and by decoding the DNA scrambled matrices multiple secrets are
reconstructed without loss The simulation results and the security analysis prove that this
algorithm is perfect and produces results with better PSNR value The correlation co-
efficient shows that this also has the ability of resisting various attacks
Advantages
1Security is better
2Resistance against Attack
9
4)Della Babya Jitha Thomasa Gisny Augustinea Elsa Georgea Neenu
Rosia Michaela (2014) ldquoA Novel DWT based Image Securing Method using
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Steganography is a data hiding technique that is widely used in various
information securing applications Steganography transmits data by hiding the existence
of the message so that a viewer cannot identify the transmission of message and hence
not able to decrypt it This work proposes a data securing technique that is used for
hiding multiple color images into a single color image using the Discrete Wavelet
Transform The cover image is split up into R G and B planes Secret images are
embedded into these planes An N-level decomposition of the cover image and the secret
images are done and some frequency components of the same are combined Secret
images are then extracted from the stego image Here the stego image obtained has a less
perceptible changes compared to the original image with high overall security
Advantages
1Less perceptible changes
2Overall security is high
10
5)Prasenjit Dasa Subhrajyoti Deba Nirmalya Kara Baby
Bhattacharyaa(2014)ldquoAn Improved DNA based Dual Cover
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Dual cover steganography is an evolving technique in the field of covert
data transmission This paper focuses on the concept of using a theoretical single stranded
DNA (ssDNA) as a primary cover which is extracted from an inconspicuous cover
image They have analyzed the security loopholes and performance issues of the existing
algorithm and proposed an improved algorithm on the same basisthey use 2D Logistic
map for encrypting the cover imageThen overall encryption is RC43 types of encryption
is generally used Performance of both the algorithms are tested against several visual
and statistical attacks and parameterized in terms of both security and capacity The
comparison shows that the proposed improvements provide better overall security
Advantages
1 Robustness against various attack
2 Performance measure are calculated
3 Data hiding improves security
11
6) Bhavana Agrawal Himani Agrawal ( May-2013) ldquoImplementation of AES
and RSA Using Chaos systemrdquo International Journal of Scientific amp
Engineering Research Vol 4 No 5
This paper presents two cryptographic algorithm AES and RSA Using Chaos
Chaos has attracted much attention in the field of cryptography It describes a system
which is sensitive to initial condition It generates apparently random behavior but at the
same time is completely deterministic Chaos function is used to increase the complexity
and Security of the SystemAES and RSA are the two cryptographic algorithms In AES
we apply the Chaos on S-box where as in RSA we mix the plaintext with Chaos sequence
First then apply for encryption and decryption After Implementing AES and RSA they
compare both the technique on the basis of speed
Advantages
1Chaos function is used to improve complexity
2The speed has been improved with combined technique of AES and RSA along with
chaos technique
12
7) Pahrul Irfan Yudi Prayudi Imam Riadi ( August 2015) ldquo Image
Encryption using Combination of Chaotic System and Rivers Shamir
Adleman (RSA)rdquo International Journal of Computer Applications Vol 123
No6
Security and confidentiality of data or information at the present time has
become an important concern Advanced methods for secure transmission storage and
retrieval of digital images are increasingly needed for a number of military medical
homeland security and other applications Various kinds of techniques for increase
security data or information already is developed one common way is by cryptographic
techniques Cryptography is science to maintain the security of the message by changing
data or information into a different form so the message cannot be recognized To
compensate for increasing computing speeds increases it takes more than one encryption
algorithm to improve security of digital images One way is by using algorithms to
double cryptography do encryption and decryption Cryptographic algorithm often used
today and the proven strength specially the digital image is Algorithm with Chaos
system To improve security at the image then we use Additional algorithms namely
Rivers algorithm Shamir Adleman (RSA) which known as the standard of cryptography
algorithms This research aims to optimize security bitmap image format by combining
the two algorithms namely Chaos-based algorithms and RSA algorithm into one
application Experiments conducted show that the proposed algorithm possesses robust
security features such as fairly uniform distribution high sensitivity to both keys and
plain images almost ideal entropy and the ability to highly de-correlate adjacent pixels
in the cipher images Furthermore it has a large key space and transform image to pure
text file which greatly increases its security for image encryption
Advantages
1 It aims to optimize security bitmap image format by combining the two algorithms
namely Chaos-based algorithms and RSA algorithm into one application
13
8) Weiming Zhang KedeMa NenghaiYu(2013) ldquo Reversibility improved
data hiding in encrypted imagesrdquo School of Information Science and
Technology
A novel reversible data hiding technique in encrypted images is presented in this
paper Instead of embedding data in encrypted images directly some pixels are estimated
before encryption so that additional data can be embedded in the estimating errors A
bench mark encryption algorithm (eg AES) is applied to the rest pixels of the image and
a special encryption scheme is designed to encrypt the estimating errors Without the
encryption key one cannot get access to the original image However provided with the
data hiding key only he can embed in or extract from the encrypted image additional data
without knowledge about the original image Moreover the data extraction and image
recovery are free of errors for all images Experiments demonstrate the feasibility and
efficiency of the proposed method especially in aspect of embedding rate versus Peak
Signal-to-Noise Ratio (PSNR)
The paper proposes a novel method to significantly improve the performance by
reversing the order of encryption and vacating room In the light of this idea we empty
out room prior to image encryption by shifting the histogram of estimating errors of some
pixels and the emptied out room will be used for data hiding The proposed method is
composed of four primary steps vacating room and encrypting image data hiding in the
encrypted image data extraction and image recovery Two different schemes extraction
before decryption and decryption before extraction are raised to cope with different
applications
Advantages
(i) Achieves excellent performance in three aspects complete reversibility PSNR
under given embedding rate separability between data higher extraction and
image decryption
14
CHAPTER 3
PROPOSED METHODOLOGY
The proposed data hiding scheme aims at the security of the hidden data
Embedding is performed in spatial domain The data to be embedded is converted into
binary form from ASCII code using chaos encryption and is embedded into the cover
image obtained after 2D logistic map This embedded image is secured using symmetric
key (K1)They are converted into DNA sequence to provide additional level of security
The hidden data can be extracted from the cover image accurately with the help of
decryption techniques and secret key (K1) The cover image can also be extracted
without any distortion The fig 31 shows the workflow
Fig 31 Work Flow Diagram
SECRET DATA
COVER IMAGE
CHAOTIC
ENCRYPTION
ENCRY 2D LOGISTIC
ENCRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
CHAOTIC
DECRYPTION
ENCRY
SECRET DATA
COVER IMAGE 2D LOGISTIC
DECRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
15
31 Chaotic Encryption
Chaotic cryptography is the application of the mathematical chaos theory to the
practice of the cryptography the study or techniques used to privately and securely
transmit information with the presence of an third-party or adversary The use of chaos
or randomness in cryptography has long been sought after by entities wanting a new way
to encrypt messages However because of the lack of thorough provable security
properties and low acceptable performance chaotic cryptography has encountered
setbacksIn order to use chaos theory acceptably in cryptography they must first be
mapped to each other Properties in chaotic systems and cryptographic primitives share
unique characteristics that allow for the chaotic systems to be applied to cryptography If
chaotic parameters as well as cryptographic keys can be mapped symmetrically or
mapped to produce acceptable and functional outputs it will make it next to impossible
for an adversary to find the outputs without any knowledge the initial values Since
chaotic maps in a real life scenario require a set of numbers that are limited they may in
fact have no real purpose in a cryptosystem if the chaotic behavior can be predicted To
counter this possibility there exists simple to advanced ciphers Chaos theory used in
cryptosystems for commercial implementation has proven to be unsuccessful mainly
because a chaos theories‟ requirement to use intervals of real numbers Given enough
resources and time an adversary could be able to predict functional outcomes Since
chaotic cryptosystems have no root in number theory this would make it difficult or
impossible to implement therefore impractical
32 The RSA Algorithm
The RSA cryptosystem named after its inventors R Rivest A Shamir and L
Adleman is the most widely used public key Cryptosystem It may be used to provide
both secrecy and digital signatures and its security is based on the intractability of the
integer factorizationThe RSA algorithm involves three steps key generation encryption
and decryption
16
321 Key Generation
RSA involves a public key and a private key The public key can be known to
everyone and is used for encrypting messages Messages encrypted with the public key
can only be decrypted in a reasonable amount of time using the private key The keys for
the RSA algorithm are generated the following way To generate the two keys choose
two random large prime numbers p and q For maximum security choose p and q of
equal length Then randomly choose the encryption key e such that e and ( p minus1) (q minus1)
are relatively prime Finally use the extended Euclidean algorithm to compute the
decryption key d such that
d= e-1
mod ( (p-1) (q-1))
Note that d and n are also relatively prime The numbers e and K are the public
key the number d is the private key The two primes p and q are no longer needed They
should be discarded but never revealed
322 Encryption
Firstly receiver transmits her public key (n e) to sender and keeps the private key
secret If sender wishes to send message M to receiver Sender change the message M in
to integer m such that 0 le mltn Then sender computes the cipher text c corresponding to
Cequiv me
(mod n)
323 Decryption
Receiver can recover M from c by using private key exponent d via computing
M equiv cd
(mod n)
17
Algorithm
1Select any two prime numbers say (pq)
2Compute n=pq and also compute empty(119899)=(p-1)(q-1)
3Choose e such that 1ltelt empty(119899)
4Choose d such that (de)mod empty(119899)=1
5Public key is (en) and Private key is (dn)
6 If egt=2 then check i==1 if so return 1 else return 0
7In a iteration check for e(i)==1 if so take mod function of message with n
8Message is converted to cipher text in ASCII form with the key generated
9The cipher data in ASCII form is converted to binary form
33 2D Logistic Encryption
The chaotic system is a deterministic nonlinear system It possesses a varied
characteristics such as high sensitivity to initial conditions and system parameters
random-like behaviors and so forth Chaotic sequences produced by chaotic maps are
pseudo-random sequences their structures are very complex and difficult to be analyzed
and predicted In other words chaotic systems can improve the security of encryption
systems Thus it is advisable to encrypt digital image with chaotic systems There are
two chaotic maps one is logistic map and the other is 2D logistic map In the proposed
work 2D logistic map is used
Logistic map is an example for chaotic map and it is described as follows
x(n+1)=μ x(n)(1minusx(n))
μ is a positive constant sometimes known as the biotic potential gives the so-called
logistic map x(n) is series of values used to plot Logistic mapwhere μ isin [04] x(n) isin
(01) and n = 01 2hellip The research result shows that the system is in chaotic state
under the condition that 356994 lt μ le 4
18
2D logistic map is described in as follows
119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2
119910119894+1 = 1205832 lowast 119910119894(1 minus 119910119894) + 1205732((119909119894)2 + 119909119894 lowast 119910119894)
Where z(xy) is the logistic map 1205831 and 1205832 are positive constant sometimes known as the
biotic potential gives the so-called logistic map x be the position of images in x axis
and y be the position of images in y axis 1205731 and 1205732 are the correlation constantsWhen
275 lt μ1 le 34 275 lt μ2 le 345 015 lt 1205731le 021 and 013 lt 1205732 le 015 the system is in
chaotic state and can generate two chaotic sequences in the region (01] Due to the
system parameter γ1 and γ2 which have smaller value range we set γ1 = 017 and γ2 =
014 other parameters can be seen as secret keys
Algorithm
1A random key is generated in binary form ( 1times256) and it is stored in a array
2The random key generated is translated to map format using block processing (4times4)
3 The row and column wise transformation is carried out
4The key is now used to encrypt the cover image
52D logistic image undergoes substitution and permutation (column and row wise
shuffling is done)
34 DNA Sequence
A single DNA sequence is made up of four nucleic acid
bases A (adenine) C (cytosine) G (guanine) and T (thymine) where A and T are
complements and C and G are complements Let binary number 0 and 1 be
complements so 00 and 11 are complements and 01 and 10 are complements Thus we
can use these four bases A T G and C to encode 01 10 00 and 11 respectively The
encoding method still satisfies the Watson-Crick complement rule Usually each pixel
value of the 8 bit grey image can be expressed to 8 bits binary stream The binary stream
can be encoded to a DNA sequence whose length is 4 For example if the first pixel
19
value of the original image is 75 convert it into a binary stream [01001011] By using the
above DNA encoding rule to encode the stream we can get a DNA sequence [AGTC]
whereas we use A T G and C to express 01 10 00 and 11 respectively We can get a
binary sequence [01001011]
35 Attacks
The steganographic algorithm is used to embed secret messages into cover
image To obtain stego image while exchanging these stego-image through the public
communication channel various attacks have been made The are generally classified
into two types intentional or unintentional attacks Examples of unintentional attacks are
transmission errors lossy compression and changing the visual properties of the stego
document Intentional attacks on the other hand are deliberate attempts to distinguish
stego-objects from unmodified objects and thus detect the presence of covert
communication Attack methods generally exploit the fact that embedding information
usually changes the statistical properties of the objects compared to typical unmodified
objects In this proposed algorithm various attacks have been applied on the encrypted
image They are as follows
1)Shearing
2) Image Scaling
3) Image Rotating
4) Image color reduction
5) Image blurred
6) Image flip
7) cropping and intensity transformation
8) Image sharpening
9) Gaussian Noise and filtering
10) Image Contrast
11) Speckle Noise and Filtering
20
351 Shearing
The image is resized with the scale factor Resized image is rotated with
angle(theta)Finally spatial transformation from control point pairs is implemented
For example
Scale factor 09
Theta10
Fig 32 Shearing Image
352 Image Scaling
It resizes the image with a scale factor and rotation is performed It rotates the
image by angle (degrees) in a counterclockwise direction around its center point To
rotate the image clockwise specify a negative value for angle It makes the output image
large enough to contain the entire rotated image It uses nearest neighbour interpolation
setting the values of pixels in Output image that are outside the rotated image to 0 (zero)
For example
Scale Factor07
Theta30
scaling Image
21
Fig 33 Scaling Image
353 Rotation
It rotates the image by angle degrees in a counterclockwise direction around its
center point To rotate the image clockwise specify a negative value for angle It makes
the output image large enough to contain the entire rotated image It uses nearest
neighbour interpolation setting the values of pixels in Output image that are outside the
rotated image to 0 (zero)
For Example
Theta180
Fig 34 Rotation Image
354 Colour Reduced Image
It creates an indexed image approximation of the RGB image in the array RGB by
dithering the colors in the colormap map The colormap cannot have more than 65536
resized and rotated image
Rotated image
22
colors
For Example
Indexed image with 32 Colors
Fig 35 Colour Reduced Image
355 Blur Image
The image is blurred by using N-D filtering of multidimensional images It filters
the multidimensional array of original image with the multidimensional filter The array
of original image can be logical or a nonsparse numeric array of any class and dimension
The result image has the same size and class as of original image
Fig 36 Blur Image
Color reduced image
50 100 150 200 250 300 350 400 450 500
50
100
150
200
250
300
350
400
450
500
Blurred image
23
356 Flipped Image
It flips the image upside down Flipping is used to invert the image
Fig 37 Flipped Image
357 Cropped Image
It creates an interactive crop image tool associated with the image displayed in the
current figure called the target image The crop image tool is a movable resizable
rectangle that you can position interactively using the mouse When the crop image tool
is active the pointer changes to cross hairs when it is moved over the target image
Using the mouse image to be cropped can be specified by clicking and dragging the
mouse The crop rectangle using the mouse can be moved or resized When sizing and
positioning of the crop rectangle is finished create the cropped image by double-clicking
the left mouse button or by choosing crop image from the context menu Image cropping
returns the cropped image
Flipped image
24
Fig 38 Cropped Image
358 Intensity Transformation Adjust
It maps the intensity values in grayscale image to new values in resultant image
such that 1 of data is saturated at low and high intensity of original image This
increases the contrast of the output image
Fig 39 Intensity Transformation Image
Cropped Image
Intensity Transformation
25
359 Sharpening
Input array values outside the bounds of the array are assumed to equal the nearest
array border value The image is sharpened by using N-D filtering of multidimensional
images It filters the multidimensional array of original image with the multidimensional
filter The array of original image can be logical or a nonsparse numeric array of any
class and dimension The result image has the same size and class as of original image
Fig 310 Sharpened Image
3510 Gaussian Noise and Median Filtering
It adds Gaussian noise to the images Gaussian white noise have constant mean
and variance The noise added image is filtered using Median Filtering Median filtering
is a nonlinear operation often used in image processing to reduce salt and pepper noise
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Sharpened Image
26
Fig 311 Gaussian Noise and Median Filter Image
3511 Histogram of contrast image
It enhances the contrast of images by transforming the values in an intensity
image or the values in the colormap of an indexed image so that the histogram of the
output image approximately matches a specified histogram
Fig 312 Contrast Image
Gaussian Noise
Median Filtering
Contrast Image
27
Fig 313 Histogram of Contrast Image
3512 Speckle noise and Median Filtering
It adds multiplicative noise to the image I using the equation J = I+nI where n is
uniformly distributed random noise with mean 0 and variance v The default for v is 004
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Fig 314 Speckle Noise and Median Filter Image
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Histogram of Contrast Image
0 50 100 150 200 250
Speckle Noise
Median Filtering
28
36 Proposed Algorithm
Step1 Enter two keys a private key and a public key through which the RSA algorithm is
performed
Step2These key are used for encrypting the secret data using chaotic algorithm with
threshold of 2
Step3The secret data is converted into binary format from ASCII code
Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied
Step5The encryption is carried out with the key generated randomly in binary(1times256)
Step6 The 2D logistic substitution and permutation are carried out
Step7The resulting binary sequence is added with the encrypted text in LSB
Step8The image is converted to DNA sequence and transmitted
Step9 Various Attacks have been applied on the resultant image
Step10The inverse process is carried out to retrieve the original image and data
Step11The Performance Metrics have been calculated
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
9
4)Della Babya Jitha Thomasa Gisny Augustinea Elsa Georgea Neenu
Rosia Michaela (2014) ldquoA Novel DWT based Image Securing Method using
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Steganography is a data hiding technique that is widely used in various
information securing applications Steganography transmits data by hiding the existence
of the message so that a viewer cannot identify the transmission of message and hence
not able to decrypt it This work proposes a data securing technique that is used for
hiding multiple color images into a single color image using the Discrete Wavelet
Transform The cover image is split up into R G and B planes Secret images are
embedded into these planes An N-level decomposition of the cover image and the secret
images are done and some frequency components of the same are combined Secret
images are then extracted from the stego image Here the stego image obtained has a less
perceptible changes compared to the original image with high overall security
Advantages
1Less perceptible changes
2Overall security is high
10
5)Prasenjit Dasa Subhrajyoti Deba Nirmalya Kara Baby
Bhattacharyaa(2014)ldquoAn Improved DNA based Dual Cover
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Dual cover steganography is an evolving technique in the field of covert
data transmission This paper focuses on the concept of using a theoretical single stranded
DNA (ssDNA) as a primary cover which is extracted from an inconspicuous cover
image They have analyzed the security loopholes and performance issues of the existing
algorithm and proposed an improved algorithm on the same basisthey use 2D Logistic
map for encrypting the cover imageThen overall encryption is RC43 types of encryption
is generally used Performance of both the algorithms are tested against several visual
and statistical attacks and parameterized in terms of both security and capacity The
comparison shows that the proposed improvements provide better overall security
Advantages
1 Robustness against various attack
2 Performance measure are calculated
3 Data hiding improves security
11
6) Bhavana Agrawal Himani Agrawal ( May-2013) ldquoImplementation of AES
and RSA Using Chaos systemrdquo International Journal of Scientific amp
Engineering Research Vol 4 No 5
This paper presents two cryptographic algorithm AES and RSA Using Chaos
Chaos has attracted much attention in the field of cryptography It describes a system
which is sensitive to initial condition It generates apparently random behavior but at the
same time is completely deterministic Chaos function is used to increase the complexity
and Security of the SystemAES and RSA are the two cryptographic algorithms In AES
we apply the Chaos on S-box where as in RSA we mix the plaintext with Chaos sequence
First then apply for encryption and decryption After Implementing AES and RSA they
compare both the technique on the basis of speed
Advantages
1Chaos function is used to improve complexity
2The speed has been improved with combined technique of AES and RSA along with
chaos technique
12
7) Pahrul Irfan Yudi Prayudi Imam Riadi ( August 2015) ldquo Image
Encryption using Combination of Chaotic System and Rivers Shamir
Adleman (RSA)rdquo International Journal of Computer Applications Vol 123
No6
Security and confidentiality of data or information at the present time has
become an important concern Advanced methods for secure transmission storage and
retrieval of digital images are increasingly needed for a number of military medical
homeland security and other applications Various kinds of techniques for increase
security data or information already is developed one common way is by cryptographic
techniques Cryptography is science to maintain the security of the message by changing
data or information into a different form so the message cannot be recognized To
compensate for increasing computing speeds increases it takes more than one encryption
algorithm to improve security of digital images One way is by using algorithms to
double cryptography do encryption and decryption Cryptographic algorithm often used
today and the proven strength specially the digital image is Algorithm with Chaos
system To improve security at the image then we use Additional algorithms namely
Rivers algorithm Shamir Adleman (RSA) which known as the standard of cryptography
algorithms This research aims to optimize security bitmap image format by combining
the two algorithms namely Chaos-based algorithms and RSA algorithm into one
application Experiments conducted show that the proposed algorithm possesses robust
security features such as fairly uniform distribution high sensitivity to both keys and
plain images almost ideal entropy and the ability to highly de-correlate adjacent pixels
in the cipher images Furthermore it has a large key space and transform image to pure
text file which greatly increases its security for image encryption
Advantages
1 It aims to optimize security bitmap image format by combining the two algorithms
namely Chaos-based algorithms and RSA algorithm into one application
13
8) Weiming Zhang KedeMa NenghaiYu(2013) ldquo Reversibility improved
data hiding in encrypted imagesrdquo School of Information Science and
Technology
A novel reversible data hiding technique in encrypted images is presented in this
paper Instead of embedding data in encrypted images directly some pixels are estimated
before encryption so that additional data can be embedded in the estimating errors A
bench mark encryption algorithm (eg AES) is applied to the rest pixels of the image and
a special encryption scheme is designed to encrypt the estimating errors Without the
encryption key one cannot get access to the original image However provided with the
data hiding key only he can embed in or extract from the encrypted image additional data
without knowledge about the original image Moreover the data extraction and image
recovery are free of errors for all images Experiments demonstrate the feasibility and
efficiency of the proposed method especially in aspect of embedding rate versus Peak
Signal-to-Noise Ratio (PSNR)
The paper proposes a novel method to significantly improve the performance by
reversing the order of encryption and vacating room In the light of this idea we empty
out room prior to image encryption by shifting the histogram of estimating errors of some
pixels and the emptied out room will be used for data hiding The proposed method is
composed of four primary steps vacating room and encrypting image data hiding in the
encrypted image data extraction and image recovery Two different schemes extraction
before decryption and decryption before extraction are raised to cope with different
applications
Advantages
(i) Achieves excellent performance in three aspects complete reversibility PSNR
under given embedding rate separability between data higher extraction and
image decryption
14
CHAPTER 3
PROPOSED METHODOLOGY
The proposed data hiding scheme aims at the security of the hidden data
Embedding is performed in spatial domain The data to be embedded is converted into
binary form from ASCII code using chaos encryption and is embedded into the cover
image obtained after 2D logistic map This embedded image is secured using symmetric
key (K1)They are converted into DNA sequence to provide additional level of security
The hidden data can be extracted from the cover image accurately with the help of
decryption techniques and secret key (K1) The cover image can also be extracted
without any distortion The fig 31 shows the workflow
Fig 31 Work Flow Diagram
SECRET DATA
COVER IMAGE
CHAOTIC
ENCRYPTION
ENCRY 2D LOGISTIC
ENCRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
CHAOTIC
DECRYPTION
ENCRY
SECRET DATA
COVER IMAGE 2D LOGISTIC
DECRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
15
31 Chaotic Encryption
Chaotic cryptography is the application of the mathematical chaos theory to the
practice of the cryptography the study or techniques used to privately and securely
transmit information with the presence of an third-party or adversary The use of chaos
or randomness in cryptography has long been sought after by entities wanting a new way
to encrypt messages However because of the lack of thorough provable security
properties and low acceptable performance chaotic cryptography has encountered
setbacksIn order to use chaos theory acceptably in cryptography they must first be
mapped to each other Properties in chaotic systems and cryptographic primitives share
unique characteristics that allow for the chaotic systems to be applied to cryptography If
chaotic parameters as well as cryptographic keys can be mapped symmetrically or
mapped to produce acceptable and functional outputs it will make it next to impossible
for an adversary to find the outputs without any knowledge the initial values Since
chaotic maps in a real life scenario require a set of numbers that are limited they may in
fact have no real purpose in a cryptosystem if the chaotic behavior can be predicted To
counter this possibility there exists simple to advanced ciphers Chaos theory used in
cryptosystems for commercial implementation has proven to be unsuccessful mainly
because a chaos theories‟ requirement to use intervals of real numbers Given enough
resources and time an adversary could be able to predict functional outcomes Since
chaotic cryptosystems have no root in number theory this would make it difficult or
impossible to implement therefore impractical
32 The RSA Algorithm
The RSA cryptosystem named after its inventors R Rivest A Shamir and L
Adleman is the most widely used public key Cryptosystem It may be used to provide
both secrecy and digital signatures and its security is based on the intractability of the
integer factorizationThe RSA algorithm involves three steps key generation encryption
and decryption
16
321 Key Generation
RSA involves a public key and a private key The public key can be known to
everyone and is used for encrypting messages Messages encrypted with the public key
can only be decrypted in a reasonable amount of time using the private key The keys for
the RSA algorithm are generated the following way To generate the two keys choose
two random large prime numbers p and q For maximum security choose p and q of
equal length Then randomly choose the encryption key e such that e and ( p minus1) (q minus1)
are relatively prime Finally use the extended Euclidean algorithm to compute the
decryption key d such that
d= e-1
mod ( (p-1) (q-1))
Note that d and n are also relatively prime The numbers e and K are the public
key the number d is the private key The two primes p and q are no longer needed They
should be discarded but never revealed
322 Encryption
Firstly receiver transmits her public key (n e) to sender and keeps the private key
secret If sender wishes to send message M to receiver Sender change the message M in
to integer m such that 0 le mltn Then sender computes the cipher text c corresponding to
Cequiv me
(mod n)
323 Decryption
Receiver can recover M from c by using private key exponent d via computing
M equiv cd
(mod n)
17
Algorithm
1Select any two prime numbers say (pq)
2Compute n=pq and also compute empty(119899)=(p-1)(q-1)
3Choose e such that 1ltelt empty(119899)
4Choose d such that (de)mod empty(119899)=1
5Public key is (en) and Private key is (dn)
6 If egt=2 then check i==1 if so return 1 else return 0
7In a iteration check for e(i)==1 if so take mod function of message with n
8Message is converted to cipher text in ASCII form with the key generated
9The cipher data in ASCII form is converted to binary form
33 2D Logistic Encryption
The chaotic system is a deterministic nonlinear system It possesses a varied
characteristics such as high sensitivity to initial conditions and system parameters
random-like behaviors and so forth Chaotic sequences produced by chaotic maps are
pseudo-random sequences their structures are very complex and difficult to be analyzed
and predicted In other words chaotic systems can improve the security of encryption
systems Thus it is advisable to encrypt digital image with chaotic systems There are
two chaotic maps one is logistic map and the other is 2D logistic map In the proposed
work 2D logistic map is used
Logistic map is an example for chaotic map and it is described as follows
x(n+1)=μ x(n)(1minusx(n))
μ is a positive constant sometimes known as the biotic potential gives the so-called
logistic map x(n) is series of values used to plot Logistic mapwhere μ isin [04] x(n) isin
(01) and n = 01 2hellip The research result shows that the system is in chaotic state
under the condition that 356994 lt μ le 4
18
2D logistic map is described in as follows
119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2
119910119894+1 = 1205832 lowast 119910119894(1 minus 119910119894) + 1205732((119909119894)2 + 119909119894 lowast 119910119894)
Where z(xy) is the logistic map 1205831 and 1205832 are positive constant sometimes known as the
biotic potential gives the so-called logistic map x be the position of images in x axis
and y be the position of images in y axis 1205731 and 1205732 are the correlation constantsWhen
275 lt μ1 le 34 275 lt μ2 le 345 015 lt 1205731le 021 and 013 lt 1205732 le 015 the system is in
chaotic state and can generate two chaotic sequences in the region (01] Due to the
system parameter γ1 and γ2 which have smaller value range we set γ1 = 017 and γ2 =
014 other parameters can be seen as secret keys
Algorithm
1A random key is generated in binary form ( 1times256) and it is stored in a array
2The random key generated is translated to map format using block processing (4times4)
3 The row and column wise transformation is carried out
4The key is now used to encrypt the cover image
52D logistic image undergoes substitution and permutation (column and row wise
shuffling is done)
34 DNA Sequence
A single DNA sequence is made up of four nucleic acid
bases A (adenine) C (cytosine) G (guanine) and T (thymine) where A and T are
complements and C and G are complements Let binary number 0 and 1 be
complements so 00 and 11 are complements and 01 and 10 are complements Thus we
can use these four bases A T G and C to encode 01 10 00 and 11 respectively The
encoding method still satisfies the Watson-Crick complement rule Usually each pixel
value of the 8 bit grey image can be expressed to 8 bits binary stream The binary stream
can be encoded to a DNA sequence whose length is 4 For example if the first pixel
19
value of the original image is 75 convert it into a binary stream [01001011] By using the
above DNA encoding rule to encode the stream we can get a DNA sequence [AGTC]
whereas we use A T G and C to express 01 10 00 and 11 respectively We can get a
binary sequence [01001011]
35 Attacks
The steganographic algorithm is used to embed secret messages into cover
image To obtain stego image while exchanging these stego-image through the public
communication channel various attacks have been made The are generally classified
into two types intentional or unintentional attacks Examples of unintentional attacks are
transmission errors lossy compression and changing the visual properties of the stego
document Intentional attacks on the other hand are deliberate attempts to distinguish
stego-objects from unmodified objects and thus detect the presence of covert
communication Attack methods generally exploit the fact that embedding information
usually changes the statistical properties of the objects compared to typical unmodified
objects In this proposed algorithm various attacks have been applied on the encrypted
image They are as follows
1)Shearing
2) Image Scaling
3) Image Rotating
4) Image color reduction
5) Image blurred
6) Image flip
7) cropping and intensity transformation
8) Image sharpening
9) Gaussian Noise and filtering
10) Image Contrast
11) Speckle Noise and Filtering
20
351 Shearing
The image is resized with the scale factor Resized image is rotated with
angle(theta)Finally spatial transformation from control point pairs is implemented
For example
Scale factor 09
Theta10
Fig 32 Shearing Image
352 Image Scaling
It resizes the image with a scale factor and rotation is performed It rotates the
image by angle (degrees) in a counterclockwise direction around its center point To
rotate the image clockwise specify a negative value for angle It makes the output image
large enough to contain the entire rotated image It uses nearest neighbour interpolation
setting the values of pixels in Output image that are outside the rotated image to 0 (zero)
For example
Scale Factor07
Theta30
scaling Image
21
Fig 33 Scaling Image
353 Rotation
It rotates the image by angle degrees in a counterclockwise direction around its
center point To rotate the image clockwise specify a negative value for angle It makes
the output image large enough to contain the entire rotated image It uses nearest
neighbour interpolation setting the values of pixels in Output image that are outside the
rotated image to 0 (zero)
For Example
Theta180
Fig 34 Rotation Image
354 Colour Reduced Image
It creates an indexed image approximation of the RGB image in the array RGB by
dithering the colors in the colormap map The colormap cannot have more than 65536
resized and rotated image
Rotated image
22
colors
For Example
Indexed image with 32 Colors
Fig 35 Colour Reduced Image
355 Blur Image
The image is blurred by using N-D filtering of multidimensional images It filters
the multidimensional array of original image with the multidimensional filter The array
of original image can be logical or a nonsparse numeric array of any class and dimension
The result image has the same size and class as of original image
Fig 36 Blur Image
Color reduced image
50 100 150 200 250 300 350 400 450 500
50
100
150
200
250
300
350
400
450
500
Blurred image
23
356 Flipped Image
It flips the image upside down Flipping is used to invert the image
Fig 37 Flipped Image
357 Cropped Image
It creates an interactive crop image tool associated with the image displayed in the
current figure called the target image The crop image tool is a movable resizable
rectangle that you can position interactively using the mouse When the crop image tool
is active the pointer changes to cross hairs when it is moved over the target image
Using the mouse image to be cropped can be specified by clicking and dragging the
mouse The crop rectangle using the mouse can be moved or resized When sizing and
positioning of the crop rectangle is finished create the cropped image by double-clicking
the left mouse button or by choosing crop image from the context menu Image cropping
returns the cropped image
Flipped image
24
Fig 38 Cropped Image
358 Intensity Transformation Adjust
It maps the intensity values in grayscale image to new values in resultant image
such that 1 of data is saturated at low and high intensity of original image This
increases the contrast of the output image
Fig 39 Intensity Transformation Image
Cropped Image
Intensity Transformation
25
359 Sharpening
Input array values outside the bounds of the array are assumed to equal the nearest
array border value The image is sharpened by using N-D filtering of multidimensional
images It filters the multidimensional array of original image with the multidimensional
filter The array of original image can be logical or a nonsparse numeric array of any
class and dimension The result image has the same size and class as of original image
Fig 310 Sharpened Image
3510 Gaussian Noise and Median Filtering
It adds Gaussian noise to the images Gaussian white noise have constant mean
and variance The noise added image is filtered using Median Filtering Median filtering
is a nonlinear operation often used in image processing to reduce salt and pepper noise
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Sharpened Image
26
Fig 311 Gaussian Noise and Median Filter Image
3511 Histogram of contrast image
It enhances the contrast of images by transforming the values in an intensity
image or the values in the colormap of an indexed image so that the histogram of the
output image approximately matches a specified histogram
Fig 312 Contrast Image
Gaussian Noise
Median Filtering
Contrast Image
27
Fig 313 Histogram of Contrast Image
3512 Speckle noise and Median Filtering
It adds multiplicative noise to the image I using the equation J = I+nI where n is
uniformly distributed random noise with mean 0 and variance v The default for v is 004
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Fig 314 Speckle Noise and Median Filter Image
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Histogram of Contrast Image
0 50 100 150 200 250
Speckle Noise
Median Filtering
28
36 Proposed Algorithm
Step1 Enter two keys a private key and a public key through which the RSA algorithm is
performed
Step2These key are used for encrypting the secret data using chaotic algorithm with
threshold of 2
Step3The secret data is converted into binary format from ASCII code
Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied
Step5The encryption is carried out with the key generated randomly in binary(1times256)
Step6 The 2D logistic substitution and permutation are carried out
Step7The resulting binary sequence is added with the encrypted text in LSB
Step8The image is converted to DNA sequence and transmitted
Step9 Various Attacks have been applied on the resultant image
Step10The inverse process is carried out to retrieve the original image and data
Step11The Performance Metrics have been calculated
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
10
5)Prasenjit Dasa Subhrajyoti Deba Nirmalya Kara Baby
Bhattacharyaa(2014)ldquoAn Improved DNA based Dual Cover
Steganographyrdquo International Conference on Information and
Communication Technologies (ICICT)
Dual cover steganography is an evolving technique in the field of covert
data transmission This paper focuses on the concept of using a theoretical single stranded
DNA (ssDNA) as a primary cover which is extracted from an inconspicuous cover
image They have analyzed the security loopholes and performance issues of the existing
algorithm and proposed an improved algorithm on the same basisthey use 2D Logistic
map for encrypting the cover imageThen overall encryption is RC43 types of encryption
is generally used Performance of both the algorithms are tested against several visual
and statistical attacks and parameterized in terms of both security and capacity The
comparison shows that the proposed improvements provide better overall security
Advantages
1 Robustness against various attack
2 Performance measure are calculated
3 Data hiding improves security
11
6) Bhavana Agrawal Himani Agrawal ( May-2013) ldquoImplementation of AES
and RSA Using Chaos systemrdquo International Journal of Scientific amp
Engineering Research Vol 4 No 5
This paper presents two cryptographic algorithm AES and RSA Using Chaos
Chaos has attracted much attention in the field of cryptography It describes a system
which is sensitive to initial condition It generates apparently random behavior but at the
same time is completely deterministic Chaos function is used to increase the complexity
and Security of the SystemAES and RSA are the two cryptographic algorithms In AES
we apply the Chaos on S-box where as in RSA we mix the plaintext with Chaos sequence
First then apply for encryption and decryption After Implementing AES and RSA they
compare both the technique on the basis of speed
Advantages
1Chaos function is used to improve complexity
2The speed has been improved with combined technique of AES and RSA along with
chaos technique
12
7) Pahrul Irfan Yudi Prayudi Imam Riadi ( August 2015) ldquo Image
Encryption using Combination of Chaotic System and Rivers Shamir
Adleman (RSA)rdquo International Journal of Computer Applications Vol 123
No6
Security and confidentiality of data or information at the present time has
become an important concern Advanced methods for secure transmission storage and
retrieval of digital images are increasingly needed for a number of military medical
homeland security and other applications Various kinds of techniques for increase
security data or information already is developed one common way is by cryptographic
techniques Cryptography is science to maintain the security of the message by changing
data or information into a different form so the message cannot be recognized To
compensate for increasing computing speeds increases it takes more than one encryption
algorithm to improve security of digital images One way is by using algorithms to
double cryptography do encryption and decryption Cryptographic algorithm often used
today and the proven strength specially the digital image is Algorithm with Chaos
system To improve security at the image then we use Additional algorithms namely
Rivers algorithm Shamir Adleman (RSA) which known as the standard of cryptography
algorithms This research aims to optimize security bitmap image format by combining
the two algorithms namely Chaos-based algorithms and RSA algorithm into one
application Experiments conducted show that the proposed algorithm possesses robust
security features such as fairly uniform distribution high sensitivity to both keys and
plain images almost ideal entropy and the ability to highly de-correlate adjacent pixels
in the cipher images Furthermore it has a large key space and transform image to pure
text file which greatly increases its security for image encryption
Advantages
1 It aims to optimize security bitmap image format by combining the two algorithms
namely Chaos-based algorithms and RSA algorithm into one application
13
8) Weiming Zhang KedeMa NenghaiYu(2013) ldquo Reversibility improved
data hiding in encrypted imagesrdquo School of Information Science and
Technology
A novel reversible data hiding technique in encrypted images is presented in this
paper Instead of embedding data in encrypted images directly some pixels are estimated
before encryption so that additional data can be embedded in the estimating errors A
bench mark encryption algorithm (eg AES) is applied to the rest pixels of the image and
a special encryption scheme is designed to encrypt the estimating errors Without the
encryption key one cannot get access to the original image However provided with the
data hiding key only he can embed in or extract from the encrypted image additional data
without knowledge about the original image Moreover the data extraction and image
recovery are free of errors for all images Experiments demonstrate the feasibility and
efficiency of the proposed method especially in aspect of embedding rate versus Peak
Signal-to-Noise Ratio (PSNR)
The paper proposes a novel method to significantly improve the performance by
reversing the order of encryption and vacating room In the light of this idea we empty
out room prior to image encryption by shifting the histogram of estimating errors of some
pixels and the emptied out room will be used for data hiding The proposed method is
composed of four primary steps vacating room and encrypting image data hiding in the
encrypted image data extraction and image recovery Two different schemes extraction
before decryption and decryption before extraction are raised to cope with different
applications
Advantages
(i) Achieves excellent performance in three aspects complete reversibility PSNR
under given embedding rate separability between data higher extraction and
image decryption
14
CHAPTER 3
PROPOSED METHODOLOGY
The proposed data hiding scheme aims at the security of the hidden data
Embedding is performed in spatial domain The data to be embedded is converted into
binary form from ASCII code using chaos encryption and is embedded into the cover
image obtained after 2D logistic map This embedded image is secured using symmetric
key (K1)They are converted into DNA sequence to provide additional level of security
The hidden data can be extracted from the cover image accurately with the help of
decryption techniques and secret key (K1) The cover image can also be extracted
without any distortion The fig 31 shows the workflow
Fig 31 Work Flow Diagram
SECRET DATA
COVER IMAGE
CHAOTIC
ENCRYPTION
ENCRY 2D LOGISTIC
ENCRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
CHAOTIC
DECRYPTION
ENCRY
SECRET DATA
COVER IMAGE 2D LOGISTIC
DECRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
15
31 Chaotic Encryption
Chaotic cryptography is the application of the mathematical chaos theory to the
practice of the cryptography the study or techniques used to privately and securely
transmit information with the presence of an third-party or adversary The use of chaos
or randomness in cryptography has long been sought after by entities wanting a new way
to encrypt messages However because of the lack of thorough provable security
properties and low acceptable performance chaotic cryptography has encountered
setbacksIn order to use chaos theory acceptably in cryptography they must first be
mapped to each other Properties in chaotic systems and cryptographic primitives share
unique characteristics that allow for the chaotic systems to be applied to cryptography If
chaotic parameters as well as cryptographic keys can be mapped symmetrically or
mapped to produce acceptable and functional outputs it will make it next to impossible
for an adversary to find the outputs without any knowledge the initial values Since
chaotic maps in a real life scenario require a set of numbers that are limited they may in
fact have no real purpose in a cryptosystem if the chaotic behavior can be predicted To
counter this possibility there exists simple to advanced ciphers Chaos theory used in
cryptosystems for commercial implementation has proven to be unsuccessful mainly
because a chaos theories‟ requirement to use intervals of real numbers Given enough
resources and time an adversary could be able to predict functional outcomes Since
chaotic cryptosystems have no root in number theory this would make it difficult or
impossible to implement therefore impractical
32 The RSA Algorithm
The RSA cryptosystem named after its inventors R Rivest A Shamir and L
Adleman is the most widely used public key Cryptosystem It may be used to provide
both secrecy and digital signatures and its security is based on the intractability of the
integer factorizationThe RSA algorithm involves three steps key generation encryption
and decryption
16
321 Key Generation
RSA involves a public key and a private key The public key can be known to
everyone and is used for encrypting messages Messages encrypted with the public key
can only be decrypted in a reasonable amount of time using the private key The keys for
the RSA algorithm are generated the following way To generate the two keys choose
two random large prime numbers p and q For maximum security choose p and q of
equal length Then randomly choose the encryption key e such that e and ( p minus1) (q minus1)
are relatively prime Finally use the extended Euclidean algorithm to compute the
decryption key d such that
d= e-1
mod ( (p-1) (q-1))
Note that d and n are also relatively prime The numbers e and K are the public
key the number d is the private key The two primes p and q are no longer needed They
should be discarded but never revealed
322 Encryption
Firstly receiver transmits her public key (n e) to sender and keeps the private key
secret If sender wishes to send message M to receiver Sender change the message M in
to integer m such that 0 le mltn Then sender computes the cipher text c corresponding to
Cequiv me
(mod n)
323 Decryption
Receiver can recover M from c by using private key exponent d via computing
M equiv cd
(mod n)
17
Algorithm
1Select any two prime numbers say (pq)
2Compute n=pq and also compute empty(119899)=(p-1)(q-1)
3Choose e such that 1ltelt empty(119899)
4Choose d such that (de)mod empty(119899)=1
5Public key is (en) and Private key is (dn)
6 If egt=2 then check i==1 if so return 1 else return 0
7In a iteration check for e(i)==1 if so take mod function of message with n
8Message is converted to cipher text in ASCII form with the key generated
9The cipher data in ASCII form is converted to binary form
33 2D Logistic Encryption
The chaotic system is a deterministic nonlinear system It possesses a varied
characteristics such as high sensitivity to initial conditions and system parameters
random-like behaviors and so forth Chaotic sequences produced by chaotic maps are
pseudo-random sequences their structures are very complex and difficult to be analyzed
and predicted In other words chaotic systems can improve the security of encryption
systems Thus it is advisable to encrypt digital image with chaotic systems There are
two chaotic maps one is logistic map and the other is 2D logistic map In the proposed
work 2D logistic map is used
Logistic map is an example for chaotic map and it is described as follows
x(n+1)=μ x(n)(1minusx(n))
μ is a positive constant sometimes known as the biotic potential gives the so-called
logistic map x(n) is series of values used to plot Logistic mapwhere μ isin [04] x(n) isin
(01) and n = 01 2hellip The research result shows that the system is in chaotic state
under the condition that 356994 lt μ le 4
18
2D logistic map is described in as follows
119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2
119910119894+1 = 1205832 lowast 119910119894(1 minus 119910119894) + 1205732((119909119894)2 + 119909119894 lowast 119910119894)
Where z(xy) is the logistic map 1205831 and 1205832 are positive constant sometimes known as the
biotic potential gives the so-called logistic map x be the position of images in x axis
and y be the position of images in y axis 1205731 and 1205732 are the correlation constantsWhen
275 lt μ1 le 34 275 lt μ2 le 345 015 lt 1205731le 021 and 013 lt 1205732 le 015 the system is in
chaotic state and can generate two chaotic sequences in the region (01] Due to the
system parameter γ1 and γ2 which have smaller value range we set γ1 = 017 and γ2 =
014 other parameters can be seen as secret keys
Algorithm
1A random key is generated in binary form ( 1times256) and it is stored in a array
2The random key generated is translated to map format using block processing (4times4)
3 The row and column wise transformation is carried out
4The key is now used to encrypt the cover image
52D logistic image undergoes substitution and permutation (column and row wise
shuffling is done)
34 DNA Sequence
A single DNA sequence is made up of four nucleic acid
bases A (adenine) C (cytosine) G (guanine) and T (thymine) where A and T are
complements and C and G are complements Let binary number 0 and 1 be
complements so 00 and 11 are complements and 01 and 10 are complements Thus we
can use these four bases A T G and C to encode 01 10 00 and 11 respectively The
encoding method still satisfies the Watson-Crick complement rule Usually each pixel
value of the 8 bit grey image can be expressed to 8 bits binary stream The binary stream
can be encoded to a DNA sequence whose length is 4 For example if the first pixel
19
value of the original image is 75 convert it into a binary stream [01001011] By using the
above DNA encoding rule to encode the stream we can get a DNA sequence [AGTC]
whereas we use A T G and C to express 01 10 00 and 11 respectively We can get a
binary sequence [01001011]
35 Attacks
The steganographic algorithm is used to embed secret messages into cover
image To obtain stego image while exchanging these stego-image through the public
communication channel various attacks have been made The are generally classified
into two types intentional or unintentional attacks Examples of unintentional attacks are
transmission errors lossy compression and changing the visual properties of the stego
document Intentional attacks on the other hand are deliberate attempts to distinguish
stego-objects from unmodified objects and thus detect the presence of covert
communication Attack methods generally exploit the fact that embedding information
usually changes the statistical properties of the objects compared to typical unmodified
objects In this proposed algorithm various attacks have been applied on the encrypted
image They are as follows
1)Shearing
2) Image Scaling
3) Image Rotating
4) Image color reduction
5) Image blurred
6) Image flip
7) cropping and intensity transformation
8) Image sharpening
9) Gaussian Noise and filtering
10) Image Contrast
11) Speckle Noise and Filtering
20
351 Shearing
The image is resized with the scale factor Resized image is rotated with
angle(theta)Finally spatial transformation from control point pairs is implemented
For example
Scale factor 09
Theta10
Fig 32 Shearing Image
352 Image Scaling
It resizes the image with a scale factor and rotation is performed It rotates the
image by angle (degrees) in a counterclockwise direction around its center point To
rotate the image clockwise specify a negative value for angle It makes the output image
large enough to contain the entire rotated image It uses nearest neighbour interpolation
setting the values of pixels in Output image that are outside the rotated image to 0 (zero)
For example
Scale Factor07
Theta30
scaling Image
21
Fig 33 Scaling Image
353 Rotation
It rotates the image by angle degrees in a counterclockwise direction around its
center point To rotate the image clockwise specify a negative value for angle It makes
the output image large enough to contain the entire rotated image It uses nearest
neighbour interpolation setting the values of pixels in Output image that are outside the
rotated image to 0 (zero)
For Example
Theta180
Fig 34 Rotation Image
354 Colour Reduced Image
It creates an indexed image approximation of the RGB image in the array RGB by
dithering the colors in the colormap map The colormap cannot have more than 65536
resized and rotated image
Rotated image
22
colors
For Example
Indexed image with 32 Colors
Fig 35 Colour Reduced Image
355 Blur Image
The image is blurred by using N-D filtering of multidimensional images It filters
the multidimensional array of original image with the multidimensional filter The array
of original image can be logical or a nonsparse numeric array of any class and dimension
The result image has the same size and class as of original image
Fig 36 Blur Image
Color reduced image
50 100 150 200 250 300 350 400 450 500
50
100
150
200
250
300
350
400
450
500
Blurred image
23
356 Flipped Image
It flips the image upside down Flipping is used to invert the image
Fig 37 Flipped Image
357 Cropped Image
It creates an interactive crop image tool associated with the image displayed in the
current figure called the target image The crop image tool is a movable resizable
rectangle that you can position interactively using the mouse When the crop image tool
is active the pointer changes to cross hairs when it is moved over the target image
Using the mouse image to be cropped can be specified by clicking and dragging the
mouse The crop rectangle using the mouse can be moved or resized When sizing and
positioning of the crop rectangle is finished create the cropped image by double-clicking
the left mouse button or by choosing crop image from the context menu Image cropping
returns the cropped image
Flipped image
24
Fig 38 Cropped Image
358 Intensity Transformation Adjust
It maps the intensity values in grayscale image to new values in resultant image
such that 1 of data is saturated at low and high intensity of original image This
increases the contrast of the output image
Fig 39 Intensity Transformation Image
Cropped Image
Intensity Transformation
25
359 Sharpening
Input array values outside the bounds of the array are assumed to equal the nearest
array border value The image is sharpened by using N-D filtering of multidimensional
images It filters the multidimensional array of original image with the multidimensional
filter The array of original image can be logical or a nonsparse numeric array of any
class and dimension The result image has the same size and class as of original image
Fig 310 Sharpened Image
3510 Gaussian Noise and Median Filtering
It adds Gaussian noise to the images Gaussian white noise have constant mean
and variance The noise added image is filtered using Median Filtering Median filtering
is a nonlinear operation often used in image processing to reduce salt and pepper noise
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Sharpened Image
26
Fig 311 Gaussian Noise and Median Filter Image
3511 Histogram of contrast image
It enhances the contrast of images by transforming the values in an intensity
image or the values in the colormap of an indexed image so that the histogram of the
output image approximately matches a specified histogram
Fig 312 Contrast Image
Gaussian Noise
Median Filtering
Contrast Image
27
Fig 313 Histogram of Contrast Image
3512 Speckle noise and Median Filtering
It adds multiplicative noise to the image I using the equation J = I+nI where n is
uniformly distributed random noise with mean 0 and variance v The default for v is 004
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Fig 314 Speckle Noise and Median Filter Image
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Histogram of Contrast Image
0 50 100 150 200 250
Speckle Noise
Median Filtering
28
36 Proposed Algorithm
Step1 Enter two keys a private key and a public key through which the RSA algorithm is
performed
Step2These key are used for encrypting the secret data using chaotic algorithm with
threshold of 2
Step3The secret data is converted into binary format from ASCII code
Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied
Step5The encryption is carried out with the key generated randomly in binary(1times256)
Step6 The 2D logistic substitution and permutation are carried out
Step7The resulting binary sequence is added with the encrypted text in LSB
Step8The image is converted to DNA sequence and transmitted
Step9 Various Attacks have been applied on the resultant image
Step10The inverse process is carried out to retrieve the original image and data
Step11The Performance Metrics have been calculated
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
11
6) Bhavana Agrawal Himani Agrawal ( May-2013) ldquoImplementation of AES
and RSA Using Chaos systemrdquo International Journal of Scientific amp
Engineering Research Vol 4 No 5
This paper presents two cryptographic algorithm AES and RSA Using Chaos
Chaos has attracted much attention in the field of cryptography It describes a system
which is sensitive to initial condition It generates apparently random behavior but at the
same time is completely deterministic Chaos function is used to increase the complexity
and Security of the SystemAES and RSA are the two cryptographic algorithms In AES
we apply the Chaos on S-box where as in RSA we mix the plaintext with Chaos sequence
First then apply for encryption and decryption After Implementing AES and RSA they
compare both the technique on the basis of speed
Advantages
1Chaos function is used to improve complexity
2The speed has been improved with combined technique of AES and RSA along with
chaos technique
12
7) Pahrul Irfan Yudi Prayudi Imam Riadi ( August 2015) ldquo Image
Encryption using Combination of Chaotic System and Rivers Shamir
Adleman (RSA)rdquo International Journal of Computer Applications Vol 123
No6
Security and confidentiality of data or information at the present time has
become an important concern Advanced methods for secure transmission storage and
retrieval of digital images are increasingly needed for a number of military medical
homeland security and other applications Various kinds of techniques for increase
security data or information already is developed one common way is by cryptographic
techniques Cryptography is science to maintain the security of the message by changing
data or information into a different form so the message cannot be recognized To
compensate for increasing computing speeds increases it takes more than one encryption
algorithm to improve security of digital images One way is by using algorithms to
double cryptography do encryption and decryption Cryptographic algorithm often used
today and the proven strength specially the digital image is Algorithm with Chaos
system To improve security at the image then we use Additional algorithms namely
Rivers algorithm Shamir Adleman (RSA) which known as the standard of cryptography
algorithms This research aims to optimize security bitmap image format by combining
the two algorithms namely Chaos-based algorithms and RSA algorithm into one
application Experiments conducted show that the proposed algorithm possesses robust
security features such as fairly uniform distribution high sensitivity to both keys and
plain images almost ideal entropy and the ability to highly de-correlate adjacent pixels
in the cipher images Furthermore it has a large key space and transform image to pure
text file which greatly increases its security for image encryption
Advantages
1 It aims to optimize security bitmap image format by combining the two algorithms
namely Chaos-based algorithms and RSA algorithm into one application
13
8) Weiming Zhang KedeMa NenghaiYu(2013) ldquo Reversibility improved
data hiding in encrypted imagesrdquo School of Information Science and
Technology
A novel reversible data hiding technique in encrypted images is presented in this
paper Instead of embedding data in encrypted images directly some pixels are estimated
before encryption so that additional data can be embedded in the estimating errors A
bench mark encryption algorithm (eg AES) is applied to the rest pixels of the image and
a special encryption scheme is designed to encrypt the estimating errors Without the
encryption key one cannot get access to the original image However provided with the
data hiding key only he can embed in or extract from the encrypted image additional data
without knowledge about the original image Moreover the data extraction and image
recovery are free of errors for all images Experiments demonstrate the feasibility and
efficiency of the proposed method especially in aspect of embedding rate versus Peak
Signal-to-Noise Ratio (PSNR)
The paper proposes a novel method to significantly improve the performance by
reversing the order of encryption and vacating room In the light of this idea we empty
out room prior to image encryption by shifting the histogram of estimating errors of some
pixels and the emptied out room will be used for data hiding The proposed method is
composed of four primary steps vacating room and encrypting image data hiding in the
encrypted image data extraction and image recovery Two different schemes extraction
before decryption and decryption before extraction are raised to cope with different
applications
Advantages
(i) Achieves excellent performance in three aspects complete reversibility PSNR
under given embedding rate separability between data higher extraction and
image decryption
14
CHAPTER 3
PROPOSED METHODOLOGY
The proposed data hiding scheme aims at the security of the hidden data
Embedding is performed in spatial domain The data to be embedded is converted into
binary form from ASCII code using chaos encryption and is embedded into the cover
image obtained after 2D logistic map This embedded image is secured using symmetric
key (K1)They are converted into DNA sequence to provide additional level of security
The hidden data can be extracted from the cover image accurately with the help of
decryption techniques and secret key (K1) The cover image can also be extracted
without any distortion The fig 31 shows the workflow
Fig 31 Work Flow Diagram
SECRET DATA
COVER IMAGE
CHAOTIC
ENCRYPTION
ENCRY 2D LOGISTIC
ENCRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
CHAOTIC
DECRYPTION
ENCRY
SECRET DATA
COVER IMAGE 2D LOGISTIC
DECRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
15
31 Chaotic Encryption
Chaotic cryptography is the application of the mathematical chaos theory to the
practice of the cryptography the study or techniques used to privately and securely
transmit information with the presence of an third-party or adversary The use of chaos
or randomness in cryptography has long been sought after by entities wanting a new way
to encrypt messages However because of the lack of thorough provable security
properties and low acceptable performance chaotic cryptography has encountered
setbacksIn order to use chaos theory acceptably in cryptography they must first be
mapped to each other Properties in chaotic systems and cryptographic primitives share
unique characteristics that allow for the chaotic systems to be applied to cryptography If
chaotic parameters as well as cryptographic keys can be mapped symmetrically or
mapped to produce acceptable and functional outputs it will make it next to impossible
for an adversary to find the outputs without any knowledge the initial values Since
chaotic maps in a real life scenario require a set of numbers that are limited they may in
fact have no real purpose in a cryptosystem if the chaotic behavior can be predicted To
counter this possibility there exists simple to advanced ciphers Chaos theory used in
cryptosystems for commercial implementation has proven to be unsuccessful mainly
because a chaos theories‟ requirement to use intervals of real numbers Given enough
resources and time an adversary could be able to predict functional outcomes Since
chaotic cryptosystems have no root in number theory this would make it difficult or
impossible to implement therefore impractical
32 The RSA Algorithm
The RSA cryptosystem named after its inventors R Rivest A Shamir and L
Adleman is the most widely used public key Cryptosystem It may be used to provide
both secrecy and digital signatures and its security is based on the intractability of the
integer factorizationThe RSA algorithm involves three steps key generation encryption
and decryption
16
321 Key Generation
RSA involves a public key and a private key The public key can be known to
everyone and is used for encrypting messages Messages encrypted with the public key
can only be decrypted in a reasonable amount of time using the private key The keys for
the RSA algorithm are generated the following way To generate the two keys choose
two random large prime numbers p and q For maximum security choose p and q of
equal length Then randomly choose the encryption key e such that e and ( p minus1) (q minus1)
are relatively prime Finally use the extended Euclidean algorithm to compute the
decryption key d such that
d= e-1
mod ( (p-1) (q-1))
Note that d and n are also relatively prime The numbers e and K are the public
key the number d is the private key The two primes p and q are no longer needed They
should be discarded but never revealed
322 Encryption
Firstly receiver transmits her public key (n e) to sender and keeps the private key
secret If sender wishes to send message M to receiver Sender change the message M in
to integer m such that 0 le mltn Then sender computes the cipher text c corresponding to
Cequiv me
(mod n)
323 Decryption
Receiver can recover M from c by using private key exponent d via computing
M equiv cd
(mod n)
17
Algorithm
1Select any two prime numbers say (pq)
2Compute n=pq and also compute empty(119899)=(p-1)(q-1)
3Choose e such that 1ltelt empty(119899)
4Choose d such that (de)mod empty(119899)=1
5Public key is (en) and Private key is (dn)
6 If egt=2 then check i==1 if so return 1 else return 0
7In a iteration check for e(i)==1 if so take mod function of message with n
8Message is converted to cipher text in ASCII form with the key generated
9The cipher data in ASCII form is converted to binary form
33 2D Logistic Encryption
The chaotic system is a deterministic nonlinear system It possesses a varied
characteristics such as high sensitivity to initial conditions and system parameters
random-like behaviors and so forth Chaotic sequences produced by chaotic maps are
pseudo-random sequences their structures are very complex and difficult to be analyzed
and predicted In other words chaotic systems can improve the security of encryption
systems Thus it is advisable to encrypt digital image with chaotic systems There are
two chaotic maps one is logistic map and the other is 2D logistic map In the proposed
work 2D logistic map is used
Logistic map is an example for chaotic map and it is described as follows
x(n+1)=μ x(n)(1minusx(n))
μ is a positive constant sometimes known as the biotic potential gives the so-called
logistic map x(n) is series of values used to plot Logistic mapwhere μ isin [04] x(n) isin
(01) and n = 01 2hellip The research result shows that the system is in chaotic state
under the condition that 356994 lt μ le 4
18
2D logistic map is described in as follows
119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2
119910119894+1 = 1205832 lowast 119910119894(1 minus 119910119894) + 1205732((119909119894)2 + 119909119894 lowast 119910119894)
Where z(xy) is the logistic map 1205831 and 1205832 are positive constant sometimes known as the
biotic potential gives the so-called logistic map x be the position of images in x axis
and y be the position of images in y axis 1205731 and 1205732 are the correlation constantsWhen
275 lt μ1 le 34 275 lt μ2 le 345 015 lt 1205731le 021 and 013 lt 1205732 le 015 the system is in
chaotic state and can generate two chaotic sequences in the region (01] Due to the
system parameter γ1 and γ2 which have smaller value range we set γ1 = 017 and γ2 =
014 other parameters can be seen as secret keys
Algorithm
1A random key is generated in binary form ( 1times256) and it is stored in a array
2The random key generated is translated to map format using block processing (4times4)
3 The row and column wise transformation is carried out
4The key is now used to encrypt the cover image
52D logistic image undergoes substitution and permutation (column and row wise
shuffling is done)
34 DNA Sequence
A single DNA sequence is made up of four nucleic acid
bases A (adenine) C (cytosine) G (guanine) and T (thymine) where A and T are
complements and C and G are complements Let binary number 0 and 1 be
complements so 00 and 11 are complements and 01 and 10 are complements Thus we
can use these four bases A T G and C to encode 01 10 00 and 11 respectively The
encoding method still satisfies the Watson-Crick complement rule Usually each pixel
value of the 8 bit grey image can be expressed to 8 bits binary stream The binary stream
can be encoded to a DNA sequence whose length is 4 For example if the first pixel
19
value of the original image is 75 convert it into a binary stream [01001011] By using the
above DNA encoding rule to encode the stream we can get a DNA sequence [AGTC]
whereas we use A T G and C to express 01 10 00 and 11 respectively We can get a
binary sequence [01001011]
35 Attacks
The steganographic algorithm is used to embed secret messages into cover
image To obtain stego image while exchanging these stego-image through the public
communication channel various attacks have been made The are generally classified
into two types intentional or unintentional attacks Examples of unintentional attacks are
transmission errors lossy compression and changing the visual properties of the stego
document Intentional attacks on the other hand are deliberate attempts to distinguish
stego-objects from unmodified objects and thus detect the presence of covert
communication Attack methods generally exploit the fact that embedding information
usually changes the statistical properties of the objects compared to typical unmodified
objects In this proposed algorithm various attacks have been applied on the encrypted
image They are as follows
1)Shearing
2) Image Scaling
3) Image Rotating
4) Image color reduction
5) Image blurred
6) Image flip
7) cropping and intensity transformation
8) Image sharpening
9) Gaussian Noise and filtering
10) Image Contrast
11) Speckle Noise and Filtering
20
351 Shearing
The image is resized with the scale factor Resized image is rotated with
angle(theta)Finally spatial transformation from control point pairs is implemented
For example
Scale factor 09
Theta10
Fig 32 Shearing Image
352 Image Scaling
It resizes the image with a scale factor and rotation is performed It rotates the
image by angle (degrees) in a counterclockwise direction around its center point To
rotate the image clockwise specify a negative value for angle It makes the output image
large enough to contain the entire rotated image It uses nearest neighbour interpolation
setting the values of pixels in Output image that are outside the rotated image to 0 (zero)
For example
Scale Factor07
Theta30
scaling Image
21
Fig 33 Scaling Image
353 Rotation
It rotates the image by angle degrees in a counterclockwise direction around its
center point To rotate the image clockwise specify a negative value for angle It makes
the output image large enough to contain the entire rotated image It uses nearest
neighbour interpolation setting the values of pixels in Output image that are outside the
rotated image to 0 (zero)
For Example
Theta180
Fig 34 Rotation Image
354 Colour Reduced Image
It creates an indexed image approximation of the RGB image in the array RGB by
dithering the colors in the colormap map The colormap cannot have more than 65536
resized and rotated image
Rotated image
22
colors
For Example
Indexed image with 32 Colors
Fig 35 Colour Reduced Image
355 Blur Image
The image is blurred by using N-D filtering of multidimensional images It filters
the multidimensional array of original image with the multidimensional filter The array
of original image can be logical or a nonsparse numeric array of any class and dimension
The result image has the same size and class as of original image
Fig 36 Blur Image
Color reduced image
50 100 150 200 250 300 350 400 450 500
50
100
150
200
250
300
350
400
450
500
Blurred image
23
356 Flipped Image
It flips the image upside down Flipping is used to invert the image
Fig 37 Flipped Image
357 Cropped Image
It creates an interactive crop image tool associated with the image displayed in the
current figure called the target image The crop image tool is a movable resizable
rectangle that you can position interactively using the mouse When the crop image tool
is active the pointer changes to cross hairs when it is moved over the target image
Using the mouse image to be cropped can be specified by clicking and dragging the
mouse The crop rectangle using the mouse can be moved or resized When sizing and
positioning of the crop rectangle is finished create the cropped image by double-clicking
the left mouse button or by choosing crop image from the context menu Image cropping
returns the cropped image
Flipped image
24
Fig 38 Cropped Image
358 Intensity Transformation Adjust
It maps the intensity values in grayscale image to new values in resultant image
such that 1 of data is saturated at low and high intensity of original image This
increases the contrast of the output image
Fig 39 Intensity Transformation Image
Cropped Image
Intensity Transformation
25
359 Sharpening
Input array values outside the bounds of the array are assumed to equal the nearest
array border value The image is sharpened by using N-D filtering of multidimensional
images It filters the multidimensional array of original image with the multidimensional
filter The array of original image can be logical or a nonsparse numeric array of any
class and dimension The result image has the same size and class as of original image
Fig 310 Sharpened Image
3510 Gaussian Noise and Median Filtering
It adds Gaussian noise to the images Gaussian white noise have constant mean
and variance The noise added image is filtered using Median Filtering Median filtering
is a nonlinear operation often used in image processing to reduce salt and pepper noise
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Sharpened Image
26
Fig 311 Gaussian Noise and Median Filter Image
3511 Histogram of contrast image
It enhances the contrast of images by transforming the values in an intensity
image or the values in the colormap of an indexed image so that the histogram of the
output image approximately matches a specified histogram
Fig 312 Contrast Image
Gaussian Noise
Median Filtering
Contrast Image
27
Fig 313 Histogram of Contrast Image
3512 Speckle noise and Median Filtering
It adds multiplicative noise to the image I using the equation J = I+nI where n is
uniformly distributed random noise with mean 0 and variance v The default for v is 004
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Fig 314 Speckle Noise and Median Filter Image
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Histogram of Contrast Image
0 50 100 150 200 250
Speckle Noise
Median Filtering
28
36 Proposed Algorithm
Step1 Enter two keys a private key and a public key through which the RSA algorithm is
performed
Step2These key are used for encrypting the secret data using chaotic algorithm with
threshold of 2
Step3The secret data is converted into binary format from ASCII code
Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied
Step5The encryption is carried out with the key generated randomly in binary(1times256)
Step6 The 2D logistic substitution and permutation are carried out
Step7The resulting binary sequence is added with the encrypted text in LSB
Step8The image is converted to DNA sequence and transmitted
Step9 Various Attacks have been applied on the resultant image
Step10The inverse process is carried out to retrieve the original image and data
Step11The Performance Metrics have been calculated
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
12
7) Pahrul Irfan Yudi Prayudi Imam Riadi ( August 2015) ldquo Image
Encryption using Combination of Chaotic System and Rivers Shamir
Adleman (RSA)rdquo International Journal of Computer Applications Vol 123
No6
Security and confidentiality of data or information at the present time has
become an important concern Advanced methods for secure transmission storage and
retrieval of digital images are increasingly needed for a number of military medical
homeland security and other applications Various kinds of techniques for increase
security data or information already is developed one common way is by cryptographic
techniques Cryptography is science to maintain the security of the message by changing
data or information into a different form so the message cannot be recognized To
compensate for increasing computing speeds increases it takes more than one encryption
algorithm to improve security of digital images One way is by using algorithms to
double cryptography do encryption and decryption Cryptographic algorithm often used
today and the proven strength specially the digital image is Algorithm with Chaos
system To improve security at the image then we use Additional algorithms namely
Rivers algorithm Shamir Adleman (RSA) which known as the standard of cryptography
algorithms This research aims to optimize security bitmap image format by combining
the two algorithms namely Chaos-based algorithms and RSA algorithm into one
application Experiments conducted show that the proposed algorithm possesses robust
security features such as fairly uniform distribution high sensitivity to both keys and
plain images almost ideal entropy and the ability to highly de-correlate adjacent pixels
in the cipher images Furthermore it has a large key space and transform image to pure
text file which greatly increases its security for image encryption
Advantages
1 It aims to optimize security bitmap image format by combining the two algorithms
namely Chaos-based algorithms and RSA algorithm into one application
13
8) Weiming Zhang KedeMa NenghaiYu(2013) ldquo Reversibility improved
data hiding in encrypted imagesrdquo School of Information Science and
Technology
A novel reversible data hiding technique in encrypted images is presented in this
paper Instead of embedding data in encrypted images directly some pixels are estimated
before encryption so that additional data can be embedded in the estimating errors A
bench mark encryption algorithm (eg AES) is applied to the rest pixels of the image and
a special encryption scheme is designed to encrypt the estimating errors Without the
encryption key one cannot get access to the original image However provided with the
data hiding key only he can embed in or extract from the encrypted image additional data
without knowledge about the original image Moreover the data extraction and image
recovery are free of errors for all images Experiments demonstrate the feasibility and
efficiency of the proposed method especially in aspect of embedding rate versus Peak
Signal-to-Noise Ratio (PSNR)
The paper proposes a novel method to significantly improve the performance by
reversing the order of encryption and vacating room In the light of this idea we empty
out room prior to image encryption by shifting the histogram of estimating errors of some
pixels and the emptied out room will be used for data hiding The proposed method is
composed of four primary steps vacating room and encrypting image data hiding in the
encrypted image data extraction and image recovery Two different schemes extraction
before decryption and decryption before extraction are raised to cope with different
applications
Advantages
(i) Achieves excellent performance in three aspects complete reversibility PSNR
under given embedding rate separability between data higher extraction and
image decryption
14
CHAPTER 3
PROPOSED METHODOLOGY
The proposed data hiding scheme aims at the security of the hidden data
Embedding is performed in spatial domain The data to be embedded is converted into
binary form from ASCII code using chaos encryption and is embedded into the cover
image obtained after 2D logistic map This embedded image is secured using symmetric
key (K1)They are converted into DNA sequence to provide additional level of security
The hidden data can be extracted from the cover image accurately with the help of
decryption techniques and secret key (K1) The cover image can also be extracted
without any distortion The fig 31 shows the workflow
Fig 31 Work Flow Diagram
SECRET DATA
COVER IMAGE
CHAOTIC
ENCRYPTION
ENCRY 2D LOGISTIC
ENCRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
CHAOTIC
DECRYPTION
ENCRY
SECRET DATA
COVER IMAGE 2D LOGISTIC
DECRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
15
31 Chaotic Encryption
Chaotic cryptography is the application of the mathematical chaos theory to the
practice of the cryptography the study or techniques used to privately and securely
transmit information with the presence of an third-party or adversary The use of chaos
or randomness in cryptography has long been sought after by entities wanting a new way
to encrypt messages However because of the lack of thorough provable security
properties and low acceptable performance chaotic cryptography has encountered
setbacksIn order to use chaos theory acceptably in cryptography they must first be
mapped to each other Properties in chaotic systems and cryptographic primitives share
unique characteristics that allow for the chaotic systems to be applied to cryptography If
chaotic parameters as well as cryptographic keys can be mapped symmetrically or
mapped to produce acceptable and functional outputs it will make it next to impossible
for an adversary to find the outputs without any knowledge the initial values Since
chaotic maps in a real life scenario require a set of numbers that are limited they may in
fact have no real purpose in a cryptosystem if the chaotic behavior can be predicted To
counter this possibility there exists simple to advanced ciphers Chaos theory used in
cryptosystems for commercial implementation has proven to be unsuccessful mainly
because a chaos theories‟ requirement to use intervals of real numbers Given enough
resources and time an adversary could be able to predict functional outcomes Since
chaotic cryptosystems have no root in number theory this would make it difficult or
impossible to implement therefore impractical
32 The RSA Algorithm
The RSA cryptosystem named after its inventors R Rivest A Shamir and L
Adleman is the most widely used public key Cryptosystem It may be used to provide
both secrecy and digital signatures and its security is based on the intractability of the
integer factorizationThe RSA algorithm involves three steps key generation encryption
and decryption
16
321 Key Generation
RSA involves a public key and a private key The public key can be known to
everyone and is used for encrypting messages Messages encrypted with the public key
can only be decrypted in a reasonable amount of time using the private key The keys for
the RSA algorithm are generated the following way To generate the two keys choose
two random large prime numbers p and q For maximum security choose p and q of
equal length Then randomly choose the encryption key e such that e and ( p minus1) (q minus1)
are relatively prime Finally use the extended Euclidean algorithm to compute the
decryption key d such that
d= e-1
mod ( (p-1) (q-1))
Note that d and n are also relatively prime The numbers e and K are the public
key the number d is the private key The two primes p and q are no longer needed They
should be discarded but never revealed
322 Encryption
Firstly receiver transmits her public key (n e) to sender and keeps the private key
secret If sender wishes to send message M to receiver Sender change the message M in
to integer m such that 0 le mltn Then sender computes the cipher text c corresponding to
Cequiv me
(mod n)
323 Decryption
Receiver can recover M from c by using private key exponent d via computing
M equiv cd
(mod n)
17
Algorithm
1Select any two prime numbers say (pq)
2Compute n=pq and also compute empty(119899)=(p-1)(q-1)
3Choose e such that 1ltelt empty(119899)
4Choose d such that (de)mod empty(119899)=1
5Public key is (en) and Private key is (dn)
6 If egt=2 then check i==1 if so return 1 else return 0
7In a iteration check for e(i)==1 if so take mod function of message with n
8Message is converted to cipher text in ASCII form with the key generated
9The cipher data in ASCII form is converted to binary form
33 2D Logistic Encryption
The chaotic system is a deterministic nonlinear system It possesses a varied
characteristics such as high sensitivity to initial conditions and system parameters
random-like behaviors and so forth Chaotic sequences produced by chaotic maps are
pseudo-random sequences their structures are very complex and difficult to be analyzed
and predicted In other words chaotic systems can improve the security of encryption
systems Thus it is advisable to encrypt digital image with chaotic systems There are
two chaotic maps one is logistic map and the other is 2D logistic map In the proposed
work 2D logistic map is used
Logistic map is an example for chaotic map and it is described as follows
x(n+1)=μ x(n)(1minusx(n))
μ is a positive constant sometimes known as the biotic potential gives the so-called
logistic map x(n) is series of values used to plot Logistic mapwhere μ isin [04] x(n) isin
(01) and n = 01 2hellip The research result shows that the system is in chaotic state
under the condition that 356994 lt μ le 4
18
2D logistic map is described in as follows
119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2
119910119894+1 = 1205832 lowast 119910119894(1 minus 119910119894) + 1205732((119909119894)2 + 119909119894 lowast 119910119894)
Where z(xy) is the logistic map 1205831 and 1205832 are positive constant sometimes known as the
biotic potential gives the so-called logistic map x be the position of images in x axis
and y be the position of images in y axis 1205731 and 1205732 are the correlation constantsWhen
275 lt μ1 le 34 275 lt μ2 le 345 015 lt 1205731le 021 and 013 lt 1205732 le 015 the system is in
chaotic state and can generate two chaotic sequences in the region (01] Due to the
system parameter γ1 and γ2 which have smaller value range we set γ1 = 017 and γ2 =
014 other parameters can be seen as secret keys
Algorithm
1A random key is generated in binary form ( 1times256) and it is stored in a array
2The random key generated is translated to map format using block processing (4times4)
3 The row and column wise transformation is carried out
4The key is now used to encrypt the cover image
52D logistic image undergoes substitution and permutation (column and row wise
shuffling is done)
34 DNA Sequence
A single DNA sequence is made up of four nucleic acid
bases A (adenine) C (cytosine) G (guanine) and T (thymine) where A and T are
complements and C and G are complements Let binary number 0 and 1 be
complements so 00 and 11 are complements and 01 and 10 are complements Thus we
can use these four bases A T G and C to encode 01 10 00 and 11 respectively The
encoding method still satisfies the Watson-Crick complement rule Usually each pixel
value of the 8 bit grey image can be expressed to 8 bits binary stream The binary stream
can be encoded to a DNA sequence whose length is 4 For example if the first pixel
19
value of the original image is 75 convert it into a binary stream [01001011] By using the
above DNA encoding rule to encode the stream we can get a DNA sequence [AGTC]
whereas we use A T G and C to express 01 10 00 and 11 respectively We can get a
binary sequence [01001011]
35 Attacks
The steganographic algorithm is used to embed secret messages into cover
image To obtain stego image while exchanging these stego-image through the public
communication channel various attacks have been made The are generally classified
into two types intentional or unintentional attacks Examples of unintentional attacks are
transmission errors lossy compression and changing the visual properties of the stego
document Intentional attacks on the other hand are deliberate attempts to distinguish
stego-objects from unmodified objects and thus detect the presence of covert
communication Attack methods generally exploit the fact that embedding information
usually changes the statistical properties of the objects compared to typical unmodified
objects In this proposed algorithm various attacks have been applied on the encrypted
image They are as follows
1)Shearing
2) Image Scaling
3) Image Rotating
4) Image color reduction
5) Image blurred
6) Image flip
7) cropping and intensity transformation
8) Image sharpening
9) Gaussian Noise and filtering
10) Image Contrast
11) Speckle Noise and Filtering
20
351 Shearing
The image is resized with the scale factor Resized image is rotated with
angle(theta)Finally spatial transformation from control point pairs is implemented
For example
Scale factor 09
Theta10
Fig 32 Shearing Image
352 Image Scaling
It resizes the image with a scale factor and rotation is performed It rotates the
image by angle (degrees) in a counterclockwise direction around its center point To
rotate the image clockwise specify a negative value for angle It makes the output image
large enough to contain the entire rotated image It uses nearest neighbour interpolation
setting the values of pixels in Output image that are outside the rotated image to 0 (zero)
For example
Scale Factor07
Theta30
scaling Image
21
Fig 33 Scaling Image
353 Rotation
It rotates the image by angle degrees in a counterclockwise direction around its
center point To rotate the image clockwise specify a negative value for angle It makes
the output image large enough to contain the entire rotated image It uses nearest
neighbour interpolation setting the values of pixels in Output image that are outside the
rotated image to 0 (zero)
For Example
Theta180
Fig 34 Rotation Image
354 Colour Reduced Image
It creates an indexed image approximation of the RGB image in the array RGB by
dithering the colors in the colormap map The colormap cannot have more than 65536
resized and rotated image
Rotated image
22
colors
For Example
Indexed image with 32 Colors
Fig 35 Colour Reduced Image
355 Blur Image
The image is blurred by using N-D filtering of multidimensional images It filters
the multidimensional array of original image with the multidimensional filter The array
of original image can be logical or a nonsparse numeric array of any class and dimension
The result image has the same size and class as of original image
Fig 36 Blur Image
Color reduced image
50 100 150 200 250 300 350 400 450 500
50
100
150
200
250
300
350
400
450
500
Blurred image
23
356 Flipped Image
It flips the image upside down Flipping is used to invert the image
Fig 37 Flipped Image
357 Cropped Image
It creates an interactive crop image tool associated with the image displayed in the
current figure called the target image The crop image tool is a movable resizable
rectangle that you can position interactively using the mouse When the crop image tool
is active the pointer changes to cross hairs when it is moved over the target image
Using the mouse image to be cropped can be specified by clicking and dragging the
mouse The crop rectangle using the mouse can be moved or resized When sizing and
positioning of the crop rectangle is finished create the cropped image by double-clicking
the left mouse button or by choosing crop image from the context menu Image cropping
returns the cropped image
Flipped image
24
Fig 38 Cropped Image
358 Intensity Transformation Adjust
It maps the intensity values in grayscale image to new values in resultant image
such that 1 of data is saturated at low and high intensity of original image This
increases the contrast of the output image
Fig 39 Intensity Transformation Image
Cropped Image
Intensity Transformation
25
359 Sharpening
Input array values outside the bounds of the array are assumed to equal the nearest
array border value The image is sharpened by using N-D filtering of multidimensional
images It filters the multidimensional array of original image with the multidimensional
filter The array of original image can be logical or a nonsparse numeric array of any
class and dimension The result image has the same size and class as of original image
Fig 310 Sharpened Image
3510 Gaussian Noise and Median Filtering
It adds Gaussian noise to the images Gaussian white noise have constant mean
and variance The noise added image is filtered using Median Filtering Median filtering
is a nonlinear operation often used in image processing to reduce salt and pepper noise
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Sharpened Image
26
Fig 311 Gaussian Noise and Median Filter Image
3511 Histogram of contrast image
It enhances the contrast of images by transforming the values in an intensity
image or the values in the colormap of an indexed image so that the histogram of the
output image approximately matches a specified histogram
Fig 312 Contrast Image
Gaussian Noise
Median Filtering
Contrast Image
27
Fig 313 Histogram of Contrast Image
3512 Speckle noise and Median Filtering
It adds multiplicative noise to the image I using the equation J = I+nI where n is
uniformly distributed random noise with mean 0 and variance v The default for v is 004
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Fig 314 Speckle Noise and Median Filter Image
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Histogram of Contrast Image
0 50 100 150 200 250
Speckle Noise
Median Filtering
28
36 Proposed Algorithm
Step1 Enter two keys a private key and a public key through which the RSA algorithm is
performed
Step2These key are used for encrypting the secret data using chaotic algorithm with
threshold of 2
Step3The secret data is converted into binary format from ASCII code
Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied
Step5The encryption is carried out with the key generated randomly in binary(1times256)
Step6 The 2D logistic substitution and permutation are carried out
Step7The resulting binary sequence is added with the encrypted text in LSB
Step8The image is converted to DNA sequence and transmitted
Step9 Various Attacks have been applied on the resultant image
Step10The inverse process is carried out to retrieve the original image and data
Step11The Performance Metrics have been calculated
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
13
8) Weiming Zhang KedeMa NenghaiYu(2013) ldquo Reversibility improved
data hiding in encrypted imagesrdquo School of Information Science and
Technology
A novel reversible data hiding technique in encrypted images is presented in this
paper Instead of embedding data in encrypted images directly some pixels are estimated
before encryption so that additional data can be embedded in the estimating errors A
bench mark encryption algorithm (eg AES) is applied to the rest pixels of the image and
a special encryption scheme is designed to encrypt the estimating errors Without the
encryption key one cannot get access to the original image However provided with the
data hiding key only he can embed in or extract from the encrypted image additional data
without knowledge about the original image Moreover the data extraction and image
recovery are free of errors for all images Experiments demonstrate the feasibility and
efficiency of the proposed method especially in aspect of embedding rate versus Peak
Signal-to-Noise Ratio (PSNR)
The paper proposes a novel method to significantly improve the performance by
reversing the order of encryption and vacating room In the light of this idea we empty
out room prior to image encryption by shifting the histogram of estimating errors of some
pixels and the emptied out room will be used for data hiding The proposed method is
composed of four primary steps vacating room and encrypting image data hiding in the
encrypted image data extraction and image recovery Two different schemes extraction
before decryption and decryption before extraction are raised to cope with different
applications
Advantages
(i) Achieves excellent performance in three aspects complete reversibility PSNR
under given embedding rate separability between data higher extraction and
image decryption
14
CHAPTER 3
PROPOSED METHODOLOGY
The proposed data hiding scheme aims at the security of the hidden data
Embedding is performed in spatial domain The data to be embedded is converted into
binary form from ASCII code using chaos encryption and is embedded into the cover
image obtained after 2D logistic map This embedded image is secured using symmetric
key (K1)They are converted into DNA sequence to provide additional level of security
The hidden data can be extracted from the cover image accurately with the help of
decryption techniques and secret key (K1) The cover image can also be extracted
without any distortion The fig 31 shows the workflow
Fig 31 Work Flow Diagram
SECRET DATA
COVER IMAGE
CHAOTIC
ENCRYPTION
ENCRY 2D LOGISTIC
ENCRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
CHAOTIC
DECRYPTION
ENCRY
SECRET DATA
COVER IMAGE 2D LOGISTIC
DECRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
15
31 Chaotic Encryption
Chaotic cryptography is the application of the mathematical chaos theory to the
practice of the cryptography the study or techniques used to privately and securely
transmit information with the presence of an third-party or adversary The use of chaos
or randomness in cryptography has long been sought after by entities wanting a new way
to encrypt messages However because of the lack of thorough provable security
properties and low acceptable performance chaotic cryptography has encountered
setbacksIn order to use chaos theory acceptably in cryptography they must first be
mapped to each other Properties in chaotic systems and cryptographic primitives share
unique characteristics that allow for the chaotic systems to be applied to cryptography If
chaotic parameters as well as cryptographic keys can be mapped symmetrically or
mapped to produce acceptable and functional outputs it will make it next to impossible
for an adversary to find the outputs without any knowledge the initial values Since
chaotic maps in a real life scenario require a set of numbers that are limited they may in
fact have no real purpose in a cryptosystem if the chaotic behavior can be predicted To
counter this possibility there exists simple to advanced ciphers Chaos theory used in
cryptosystems for commercial implementation has proven to be unsuccessful mainly
because a chaos theories‟ requirement to use intervals of real numbers Given enough
resources and time an adversary could be able to predict functional outcomes Since
chaotic cryptosystems have no root in number theory this would make it difficult or
impossible to implement therefore impractical
32 The RSA Algorithm
The RSA cryptosystem named after its inventors R Rivest A Shamir and L
Adleman is the most widely used public key Cryptosystem It may be used to provide
both secrecy and digital signatures and its security is based on the intractability of the
integer factorizationThe RSA algorithm involves three steps key generation encryption
and decryption
16
321 Key Generation
RSA involves a public key and a private key The public key can be known to
everyone and is used for encrypting messages Messages encrypted with the public key
can only be decrypted in a reasonable amount of time using the private key The keys for
the RSA algorithm are generated the following way To generate the two keys choose
two random large prime numbers p and q For maximum security choose p and q of
equal length Then randomly choose the encryption key e such that e and ( p minus1) (q minus1)
are relatively prime Finally use the extended Euclidean algorithm to compute the
decryption key d such that
d= e-1
mod ( (p-1) (q-1))
Note that d and n are also relatively prime The numbers e and K are the public
key the number d is the private key The two primes p and q are no longer needed They
should be discarded but never revealed
322 Encryption
Firstly receiver transmits her public key (n e) to sender and keeps the private key
secret If sender wishes to send message M to receiver Sender change the message M in
to integer m such that 0 le mltn Then sender computes the cipher text c corresponding to
Cequiv me
(mod n)
323 Decryption
Receiver can recover M from c by using private key exponent d via computing
M equiv cd
(mod n)
17
Algorithm
1Select any two prime numbers say (pq)
2Compute n=pq and also compute empty(119899)=(p-1)(q-1)
3Choose e such that 1ltelt empty(119899)
4Choose d such that (de)mod empty(119899)=1
5Public key is (en) and Private key is (dn)
6 If egt=2 then check i==1 if so return 1 else return 0
7In a iteration check for e(i)==1 if so take mod function of message with n
8Message is converted to cipher text in ASCII form with the key generated
9The cipher data in ASCII form is converted to binary form
33 2D Logistic Encryption
The chaotic system is a deterministic nonlinear system It possesses a varied
characteristics such as high sensitivity to initial conditions and system parameters
random-like behaviors and so forth Chaotic sequences produced by chaotic maps are
pseudo-random sequences their structures are very complex and difficult to be analyzed
and predicted In other words chaotic systems can improve the security of encryption
systems Thus it is advisable to encrypt digital image with chaotic systems There are
two chaotic maps one is logistic map and the other is 2D logistic map In the proposed
work 2D logistic map is used
Logistic map is an example for chaotic map and it is described as follows
x(n+1)=μ x(n)(1minusx(n))
μ is a positive constant sometimes known as the biotic potential gives the so-called
logistic map x(n) is series of values used to plot Logistic mapwhere μ isin [04] x(n) isin
(01) and n = 01 2hellip The research result shows that the system is in chaotic state
under the condition that 356994 lt μ le 4
18
2D logistic map is described in as follows
119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2
119910119894+1 = 1205832 lowast 119910119894(1 minus 119910119894) + 1205732((119909119894)2 + 119909119894 lowast 119910119894)
Where z(xy) is the logistic map 1205831 and 1205832 are positive constant sometimes known as the
biotic potential gives the so-called logistic map x be the position of images in x axis
and y be the position of images in y axis 1205731 and 1205732 are the correlation constantsWhen
275 lt μ1 le 34 275 lt μ2 le 345 015 lt 1205731le 021 and 013 lt 1205732 le 015 the system is in
chaotic state and can generate two chaotic sequences in the region (01] Due to the
system parameter γ1 and γ2 which have smaller value range we set γ1 = 017 and γ2 =
014 other parameters can be seen as secret keys
Algorithm
1A random key is generated in binary form ( 1times256) and it is stored in a array
2The random key generated is translated to map format using block processing (4times4)
3 The row and column wise transformation is carried out
4The key is now used to encrypt the cover image
52D logistic image undergoes substitution and permutation (column and row wise
shuffling is done)
34 DNA Sequence
A single DNA sequence is made up of four nucleic acid
bases A (adenine) C (cytosine) G (guanine) and T (thymine) where A and T are
complements and C and G are complements Let binary number 0 and 1 be
complements so 00 and 11 are complements and 01 and 10 are complements Thus we
can use these four bases A T G and C to encode 01 10 00 and 11 respectively The
encoding method still satisfies the Watson-Crick complement rule Usually each pixel
value of the 8 bit grey image can be expressed to 8 bits binary stream The binary stream
can be encoded to a DNA sequence whose length is 4 For example if the first pixel
19
value of the original image is 75 convert it into a binary stream [01001011] By using the
above DNA encoding rule to encode the stream we can get a DNA sequence [AGTC]
whereas we use A T G and C to express 01 10 00 and 11 respectively We can get a
binary sequence [01001011]
35 Attacks
The steganographic algorithm is used to embed secret messages into cover
image To obtain stego image while exchanging these stego-image through the public
communication channel various attacks have been made The are generally classified
into two types intentional or unintentional attacks Examples of unintentional attacks are
transmission errors lossy compression and changing the visual properties of the stego
document Intentional attacks on the other hand are deliberate attempts to distinguish
stego-objects from unmodified objects and thus detect the presence of covert
communication Attack methods generally exploit the fact that embedding information
usually changes the statistical properties of the objects compared to typical unmodified
objects In this proposed algorithm various attacks have been applied on the encrypted
image They are as follows
1)Shearing
2) Image Scaling
3) Image Rotating
4) Image color reduction
5) Image blurred
6) Image flip
7) cropping and intensity transformation
8) Image sharpening
9) Gaussian Noise and filtering
10) Image Contrast
11) Speckle Noise and Filtering
20
351 Shearing
The image is resized with the scale factor Resized image is rotated with
angle(theta)Finally spatial transformation from control point pairs is implemented
For example
Scale factor 09
Theta10
Fig 32 Shearing Image
352 Image Scaling
It resizes the image with a scale factor and rotation is performed It rotates the
image by angle (degrees) in a counterclockwise direction around its center point To
rotate the image clockwise specify a negative value for angle It makes the output image
large enough to contain the entire rotated image It uses nearest neighbour interpolation
setting the values of pixels in Output image that are outside the rotated image to 0 (zero)
For example
Scale Factor07
Theta30
scaling Image
21
Fig 33 Scaling Image
353 Rotation
It rotates the image by angle degrees in a counterclockwise direction around its
center point To rotate the image clockwise specify a negative value for angle It makes
the output image large enough to contain the entire rotated image It uses nearest
neighbour interpolation setting the values of pixels in Output image that are outside the
rotated image to 0 (zero)
For Example
Theta180
Fig 34 Rotation Image
354 Colour Reduced Image
It creates an indexed image approximation of the RGB image in the array RGB by
dithering the colors in the colormap map The colormap cannot have more than 65536
resized and rotated image
Rotated image
22
colors
For Example
Indexed image with 32 Colors
Fig 35 Colour Reduced Image
355 Blur Image
The image is blurred by using N-D filtering of multidimensional images It filters
the multidimensional array of original image with the multidimensional filter The array
of original image can be logical or a nonsparse numeric array of any class and dimension
The result image has the same size and class as of original image
Fig 36 Blur Image
Color reduced image
50 100 150 200 250 300 350 400 450 500
50
100
150
200
250
300
350
400
450
500
Blurred image
23
356 Flipped Image
It flips the image upside down Flipping is used to invert the image
Fig 37 Flipped Image
357 Cropped Image
It creates an interactive crop image tool associated with the image displayed in the
current figure called the target image The crop image tool is a movable resizable
rectangle that you can position interactively using the mouse When the crop image tool
is active the pointer changes to cross hairs when it is moved over the target image
Using the mouse image to be cropped can be specified by clicking and dragging the
mouse The crop rectangle using the mouse can be moved or resized When sizing and
positioning of the crop rectangle is finished create the cropped image by double-clicking
the left mouse button or by choosing crop image from the context menu Image cropping
returns the cropped image
Flipped image
24
Fig 38 Cropped Image
358 Intensity Transformation Adjust
It maps the intensity values in grayscale image to new values in resultant image
such that 1 of data is saturated at low and high intensity of original image This
increases the contrast of the output image
Fig 39 Intensity Transformation Image
Cropped Image
Intensity Transformation
25
359 Sharpening
Input array values outside the bounds of the array are assumed to equal the nearest
array border value The image is sharpened by using N-D filtering of multidimensional
images It filters the multidimensional array of original image with the multidimensional
filter The array of original image can be logical or a nonsparse numeric array of any
class and dimension The result image has the same size and class as of original image
Fig 310 Sharpened Image
3510 Gaussian Noise and Median Filtering
It adds Gaussian noise to the images Gaussian white noise have constant mean
and variance The noise added image is filtered using Median Filtering Median filtering
is a nonlinear operation often used in image processing to reduce salt and pepper noise
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Sharpened Image
26
Fig 311 Gaussian Noise and Median Filter Image
3511 Histogram of contrast image
It enhances the contrast of images by transforming the values in an intensity
image or the values in the colormap of an indexed image so that the histogram of the
output image approximately matches a specified histogram
Fig 312 Contrast Image
Gaussian Noise
Median Filtering
Contrast Image
27
Fig 313 Histogram of Contrast Image
3512 Speckle noise and Median Filtering
It adds multiplicative noise to the image I using the equation J = I+nI where n is
uniformly distributed random noise with mean 0 and variance v The default for v is 004
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Fig 314 Speckle Noise and Median Filter Image
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Histogram of Contrast Image
0 50 100 150 200 250
Speckle Noise
Median Filtering
28
36 Proposed Algorithm
Step1 Enter two keys a private key and a public key through which the RSA algorithm is
performed
Step2These key are used for encrypting the secret data using chaotic algorithm with
threshold of 2
Step3The secret data is converted into binary format from ASCII code
Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied
Step5The encryption is carried out with the key generated randomly in binary(1times256)
Step6 The 2D logistic substitution and permutation are carried out
Step7The resulting binary sequence is added with the encrypted text in LSB
Step8The image is converted to DNA sequence and transmitted
Step9 Various Attacks have been applied on the resultant image
Step10The inverse process is carried out to retrieve the original image and data
Step11The Performance Metrics have been calculated
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
14
CHAPTER 3
PROPOSED METHODOLOGY
The proposed data hiding scheme aims at the security of the hidden data
Embedding is performed in spatial domain The data to be embedded is converted into
binary form from ASCII code using chaos encryption and is embedded into the cover
image obtained after 2D logistic map This embedded image is secured using symmetric
key (K1)They are converted into DNA sequence to provide additional level of security
The hidden data can be extracted from the cover image accurately with the help of
decryption techniques and secret key (K1) The cover image can also be extracted
without any distortion The fig 31 shows the workflow
Fig 31 Work Flow Diagram
SECRET DATA
COVER IMAGE
CHAOTIC
ENCRYPTION
ENCRY 2D LOGISTIC
ENCRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
CHAOTIC
DECRYPTION
ENCRY
SECRET DATA
COVER IMAGE 2D LOGISTIC
DECRYPTION
EMBEDDED
IMAGE
KEY (K1)
DNA
SEQUENCE
15
31 Chaotic Encryption
Chaotic cryptography is the application of the mathematical chaos theory to the
practice of the cryptography the study or techniques used to privately and securely
transmit information with the presence of an third-party or adversary The use of chaos
or randomness in cryptography has long been sought after by entities wanting a new way
to encrypt messages However because of the lack of thorough provable security
properties and low acceptable performance chaotic cryptography has encountered
setbacksIn order to use chaos theory acceptably in cryptography they must first be
mapped to each other Properties in chaotic systems and cryptographic primitives share
unique characteristics that allow for the chaotic systems to be applied to cryptography If
chaotic parameters as well as cryptographic keys can be mapped symmetrically or
mapped to produce acceptable and functional outputs it will make it next to impossible
for an adversary to find the outputs without any knowledge the initial values Since
chaotic maps in a real life scenario require a set of numbers that are limited they may in
fact have no real purpose in a cryptosystem if the chaotic behavior can be predicted To
counter this possibility there exists simple to advanced ciphers Chaos theory used in
cryptosystems for commercial implementation has proven to be unsuccessful mainly
because a chaos theories‟ requirement to use intervals of real numbers Given enough
resources and time an adversary could be able to predict functional outcomes Since
chaotic cryptosystems have no root in number theory this would make it difficult or
impossible to implement therefore impractical
32 The RSA Algorithm
The RSA cryptosystem named after its inventors R Rivest A Shamir and L
Adleman is the most widely used public key Cryptosystem It may be used to provide
both secrecy and digital signatures and its security is based on the intractability of the
integer factorizationThe RSA algorithm involves three steps key generation encryption
and decryption
16
321 Key Generation
RSA involves a public key and a private key The public key can be known to
everyone and is used for encrypting messages Messages encrypted with the public key
can only be decrypted in a reasonable amount of time using the private key The keys for
the RSA algorithm are generated the following way To generate the two keys choose
two random large prime numbers p and q For maximum security choose p and q of
equal length Then randomly choose the encryption key e such that e and ( p minus1) (q minus1)
are relatively prime Finally use the extended Euclidean algorithm to compute the
decryption key d such that
d= e-1
mod ( (p-1) (q-1))
Note that d and n are also relatively prime The numbers e and K are the public
key the number d is the private key The two primes p and q are no longer needed They
should be discarded but never revealed
322 Encryption
Firstly receiver transmits her public key (n e) to sender and keeps the private key
secret If sender wishes to send message M to receiver Sender change the message M in
to integer m such that 0 le mltn Then sender computes the cipher text c corresponding to
Cequiv me
(mod n)
323 Decryption
Receiver can recover M from c by using private key exponent d via computing
M equiv cd
(mod n)
17
Algorithm
1Select any two prime numbers say (pq)
2Compute n=pq and also compute empty(119899)=(p-1)(q-1)
3Choose e such that 1ltelt empty(119899)
4Choose d such that (de)mod empty(119899)=1
5Public key is (en) and Private key is (dn)
6 If egt=2 then check i==1 if so return 1 else return 0
7In a iteration check for e(i)==1 if so take mod function of message with n
8Message is converted to cipher text in ASCII form with the key generated
9The cipher data in ASCII form is converted to binary form
33 2D Logistic Encryption
The chaotic system is a deterministic nonlinear system It possesses a varied
characteristics such as high sensitivity to initial conditions and system parameters
random-like behaviors and so forth Chaotic sequences produced by chaotic maps are
pseudo-random sequences their structures are very complex and difficult to be analyzed
and predicted In other words chaotic systems can improve the security of encryption
systems Thus it is advisable to encrypt digital image with chaotic systems There are
two chaotic maps one is logistic map and the other is 2D logistic map In the proposed
work 2D logistic map is used
Logistic map is an example for chaotic map and it is described as follows
x(n+1)=μ x(n)(1minusx(n))
μ is a positive constant sometimes known as the biotic potential gives the so-called
logistic map x(n) is series of values used to plot Logistic mapwhere μ isin [04] x(n) isin
(01) and n = 01 2hellip The research result shows that the system is in chaotic state
under the condition that 356994 lt μ le 4
18
2D logistic map is described in as follows
119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2
119910119894+1 = 1205832 lowast 119910119894(1 minus 119910119894) + 1205732((119909119894)2 + 119909119894 lowast 119910119894)
Where z(xy) is the logistic map 1205831 and 1205832 are positive constant sometimes known as the
biotic potential gives the so-called logistic map x be the position of images in x axis
and y be the position of images in y axis 1205731 and 1205732 are the correlation constantsWhen
275 lt μ1 le 34 275 lt μ2 le 345 015 lt 1205731le 021 and 013 lt 1205732 le 015 the system is in
chaotic state and can generate two chaotic sequences in the region (01] Due to the
system parameter γ1 and γ2 which have smaller value range we set γ1 = 017 and γ2 =
014 other parameters can be seen as secret keys
Algorithm
1A random key is generated in binary form ( 1times256) and it is stored in a array
2The random key generated is translated to map format using block processing (4times4)
3 The row and column wise transformation is carried out
4The key is now used to encrypt the cover image
52D logistic image undergoes substitution and permutation (column and row wise
shuffling is done)
34 DNA Sequence
A single DNA sequence is made up of four nucleic acid
bases A (adenine) C (cytosine) G (guanine) and T (thymine) where A and T are
complements and C and G are complements Let binary number 0 and 1 be
complements so 00 and 11 are complements and 01 and 10 are complements Thus we
can use these four bases A T G and C to encode 01 10 00 and 11 respectively The
encoding method still satisfies the Watson-Crick complement rule Usually each pixel
value of the 8 bit grey image can be expressed to 8 bits binary stream The binary stream
can be encoded to a DNA sequence whose length is 4 For example if the first pixel
19
value of the original image is 75 convert it into a binary stream [01001011] By using the
above DNA encoding rule to encode the stream we can get a DNA sequence [AGTC]
whereas we use A T G and C to express 01 10 00 and 11 respectively We can get a
binary sequence [01001011]
35 Attacks
The steganographic algorithm is used to embed secret messages into cover
image To obtain stego image while exchanging these stego-image through the public
communication channel various attacks have been made The are generally classified
into two types intentional or unintentional attacks Examples of unintentional attacks are
transmission errors lossy compression and changing the visual properties of the stego
document Intentional attacks on the other hand are deliberate attempts to distinguish
stego-objects from unmodified objects and thus detect the presence of covert
communication Attack methods generally exploit the fact that embedding information
usually changes the statistical properties of the objects compared to typical unmodified
objects In this proposed algorithm various attacks have been applied on the encrypted
image They are as follows
1)Shearing
2) Image Scaling
3) Image Rotating
4) Image color reduction
5) Image blurred
6) Image flip
7) cropping and intensity transformation
8) Image sharpening
9) Gaussian Noise and filtering
10) Image Contrast
11) Speckle Noise and Filtering
20
351 Shearing
The image is resized with the scale factor Resized image is rotated with
angle(theta)Finally spatial transformation from control point pairs is implemented
For example
Scale factor 09
Theta10
Fig 32 Shearing Image
352 Image Scaling
It resizes the image with a scale factor and rotation is performed It rotates the
image by angle (degrees) in a counterclockwise direction around its center point To
rotate the image clockwise specify a negative value for angle It makes the output image
large enough to contain the entire rotated image It uses nearest neighbour interpolation
setting the values of pixels in Output image that are outside the rotated image to 0 (zero)
For example
Scale Factor07
Theta30
scaling Image
21
Fig 33 Scaling Image
353 Rotation
It rotates the image by angle degrees in a counterclockwise direction around its
center point To rotate the image clockwise specify a negative value for angle It makes
the output image large enough to contain the entire rotated image It uses nearest
neighbour interpolation setting the values of pixels in Output image that are outside the
rotated image to 0 (zero)
For Example
Theta180
Fig 34 Rotation Image
354 Colour Reduced Image
It creates an indexed image approximation of the RGB image in the array RGB by
dithering the colors in the colormap map The colormap cannot have more than 65536
resized and rotated image
Rotated image
22
colors
For Example
Indexed image with 32 Colors
Fig 35 Colour Reduced Image
355 Blur Image
The image is blurred by using N-D filtering of multidimensional images It filters
the multidimensional array of original image with the multidimensional filter The array
of original image can be logical or a nonsparse numeric array of any class and dimension
The result image has the same size and class as of original image
Fig 36 Blur Image
Color reduced image
50 100 150 200 250 300 350 400 450 500
50
100
150
200
250
300
350
400
450
500
Blurred image
23
356 Flipped Image
It flips the image upside down Flipping is used to invert the image
Fig 37 Flipped Image
357 Cropped Image
It creates an interactive crop image tool associated with the image displayed in the
current figure called the target image The crop image tool is a movable resizable
rectangle that you can position interactively using the mouse When the crop image tool
is active the pointer changes to cross hairs when it is moved over the target image
Using the mouse image to be cropped can be specified by clicking and dragging the
mouse The crop rectangle using the mouse can be moved or resized When sizing and
positioning of the crop rectangle is finished create the cropped image by double-clicking
the left mouse button or by choosing crop image from the context menu Image cropping
returns the cropped image
Flipped image
24
Fig 38 Cropped Image
358 Intensity Transformation Adjust
It maps the intensity values in grayscale image to new values in resultant image
such that 1 of data is saturated at low and high intensity of original image This
increases the contrast of the output image
Fig 39 Intensity Transformation Image
Cropped Image
Intensity Transformation
25
359 Sharpening
Input array values outside the bounds of the array are assumed to equal the nearest
array border value The image is sharpened by using N-D filtering of multidimensional
images It filters the multidimensional array of original image with the multidimensional
filter The array of original image can be logical or a nonsparse numeric array of any
class and dimension The result image has the same size and class as of original image
Fig 310 Sharpened Image
3510 Gaussian Noise and Median Filtering
It adds Gaussian noise to the images Gaussian white noise have constant mean
and variance The noise added image is filtered using Median Filtering Median filtering
is a nonlinear operation often used in image processing to reduce salt and pepper noise
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Sharpened Image
26
Fig 311 Gaussian Noise and Median Filter Image
3511 Histogram of contrast image
It enhances the contrast of images by transforming the values in an intensity
image or the values in the colormap of an indexed image so that the histogram of the
output image approximately matches a specified histogram
Fig 312 Contrast Image
Gaussian Noise
Median Filtering
Contrast Image
27
Fig 313 Histogram of Contrast Image
3512 Speckle noise and Median Filtering
It adds multiplicative noise to the image I using the equation J = I+nI where n is
uniformly distributed random noise with mean 0 and variance v The default for v is 004
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Fig 314 Speckle Noise and Median Filter Image
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Histogram of Contrast Image
0 50 100 150 200 250
Speckle Noise
Median Filtering
28
36 Proposed Algorithm
Step1 Enter two keys a private key and a public key through which the RSA algorithm is
performed
Step2These key are used for encrypting the secret data using chaotic algorithm with
threshold of 2
Step3The secret data is converted into binary format from ASCII code
Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied
Step5The encryption is carried out with the key generated randomly in binary(1times256)
Step6 The 2D logistic substitution and permutation are carried out
Step7The resulting binary sequence is added with the encrypted text in LSB
Step8The image is converted to DNA sequence and transmitted
Step9 Various Attacks have been applied on the resultant image
Step10The inverse process is carried out to retrieve the original image and data
Step11The Performance Metrics have been calculated
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
15
31 Chaotic Encryption
Chaotic cryptography is the application of the mathematical chaos theory to the
practice of the cryptography the study or techniques used to privately and securely
transmit information with the presence of an third-party or adversary The use of chaos
or randomness in cryptography has long been sought after by entities wanting a new way
to encrypt messages However because of the lack of thorough provable security
properties and low acceptable performance chaotic cryptography has encountered
setbacksIn order to use chaos theory acceptably in cryptography they must first be
mapped to each other Properties in chaotic systems and cryptographic primitives share
unique characteristics that allow for the chaotic systems to be applied to cryptography If
chaotic parameters as well as cryptographic keys can be mapped symmetrically or
mapped to produce acceptable and functional outputs it will make it next to impossible
for an adversary to find the outputs without any knowledge the initial values Since
chaotic maps in a real life scenario require a set of numbers that are limited they may in
fact have no real purpose in a cryptosystem if the chaotic behavior can be predicted To
counter this possibility there exists simple to advanced ciphers Chaos theory used in
cryptosystems for commercial implementation has proven to be unsuccessful mainly
because a chaos theories‟ requirement to use intervals of real numbers Given enough
resources and time an adversary could be able to predict functional outcomes Since
chaotic cryptosystems have no root in number theory this would make it difficult or
impossible to implement therefore impractical
32 The RSA Algorithm
The RSA cryptosystem named after its inventors R Rivest A Shamir and L
Adleman is the most widely used public key Cryptosystem It may be used to provide
both secrecy and digital signatures and its security is based on the intractability of the
integer factorizationThe RSA algorithm involves three steps key generation encryption
and decryption
16
321 Key Generation
RSA involves a public key and a private key The public key can be known to
everyone and is used for encrypting messages Messages encrypted with the public key
can only be decrypted in a reasonable amount of time using the private key The keys for
the RSA algorithm are generated the following way To generate the two keys choose
two random large prime numbers p and q For maximum security choose p and q of
equal length Then randomly choose the encryption key e such that e and ( p minus1) (q minus1)
are relatively prime Finally use the extended Euclidean algorithm to compute the
decryption key d such that
d= e-1
mod ( (p-1) (q-1))
Note that d and n are also relatively prime The numbers e and K are the public
key the number d is the private key The two primes p and q are no longer needed They
should be discarded but never revealed
322 Encryption
Firstly receiver transmits her public key (n e) to sender and keeps the private key
secret If sender wishes to send message M to receiver Sender change the message M in
to integer m such that 0 le mltn Then sender computes the cipher text c corresponding to
Cequiv me
(mod n)
323 Decryption
Receiver can recover M from c by using private key exponent d via computing
M equiv cd
(mod n)
17
Algorithm
1Select any two prime numbers say (pq)
2Compute n=pq and also compute empty(119899)=(p-1)(q-1)
3Choose e such that 1ltelt empty(119899)
4Choose d such that (de)mod empty(119899)=1
5Public key is (en) and Private key is (dn)
6 If egt=2 then check i==1 if so return 1 else return 0
7In a iteration check for e(i)==1 if so take mod function of message with n
8Message is converted to cipher text in ASCII form with the key generated
9The cipher data in ASCII form is converted to binary form
33 2D Logistic Encryption
The chaotic system is a deterministic nonlinear system It possesses a varied
characteristics such as high sensitivity to initial conditions and system parameters
random-like behaviors and so forth Chaotic sequences produced by chaotic maps are
pseudo-random sequences their structures are very complex and difficult to be analyzed
and predicted In other words chaotic systems can improve the security of encryption
systems Thus it is advisable to encrypt digital image with chaotic systems There are
two chaotic maps one is logistic map and the other is 2D logistic map In the proposed
work 2D logistic map is used
Logistic map is an example for chaotic map and it is described as follows
x(n+1)=μ x(n)(1minusx(n))
μ is a positive constant sometimes known as the biotic potential gives the so-called
logistic map x(n) is series of values used to plot Logistic mapwhere μ isin [04] x(n) isin
(01) and n = 01 2hellip The research result shows that the system is in chaotic state
under the condition that 356994 lt μ le 4
18
2D logistic map is described in as follows
119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2
119910119894+1 = 1205832 lowast 119910119894(1 minus 119910119894) + 1205732((119909119894)2 + 119909119894 lowast 119910119894)
Where z(xy) is the logistic map 1205831 and 1205832 are positive constant sometimes known as the
biotic potential gives the so-called logistic map x be the position of images in x axis
and y be the position of images in y axis 1205731 and 1205732 are the correlation constantsWhen
275 lt μ1 le 34 275 lt μ2 le 345 015 lt 1205731le 021 and 013 lt 1205732 le 015 the system is in
chaotic state and can generate two chaotic sequences in the region (01] Due to the
system parameter γ1 and γ2 which have smaller value range we set γ1 = 017 and γ2 =
014 other parameters can be seen as secret keys
Algorithm
1A random key is generated in binary form ( 1times256) and it is stored in a array
2The random key generated is translated to map format using block processing (4times4)
3 The row and column wise transformation is carried out
4The key is now used to encrypt the cover image
52D logistic image undergoes substitution and permutation (column and row wise
shuffling is done)
34 DNA Sequence
A single DNA sequence is made up of four nucleic acid
bases A (adenine) C (cytosine) G (guanine) and T (thymine) where A and T are
complements and C and G are complements Let binary number 0 and 1 be
complements so 00 and 11 are complements and 01 and 10 are complements Thus we
can use these four bases A T G and C to encode 01 10 00 and 11 respectively The
encoding method still satisfies the Watson-Crick complement rule Usually each pixel
value of the 8 bit grey image can be expressed to 8 bits binary stream The binary stream
can be encoded to a DNA sequence whose length is 4 For example if the first pixel
19
value of the original image is 75 convert it into a binary stream [01001011] By using the
above DNA encoding rule to encode the stream we can get a DNA sequence [AGTC]
whereas we use A T G and C to express 01 10 00 and 11 respectively We can get a
binary sequence [01001011]
35 Attacks
The steganographic algorithm is used to embed secret messages into cover
image To obtain stego image while exchanging these stego-image through the public
communication channel various attacks have been made The are generally classified
into two types intentional or unintentional attacks Examples of unintentional attacks are
transmission errors lossy compression and changing the visual properties of the stego
document Intentional attacks on the other hand are deliberate attempts to distinguish
stego-objects from unmodified objects and thus detect the presence of covert
communication Attack methods generally exploit the fact that embedding information
usually changes the statistical properties of the objects compared to typical unmodified
objects In this proposed algorithm various attacks have been applied on the encrypted
image They are as follows
1)Shearing
2) Image Scaling
3) Image Rotating
4) Image color reduction
5) Image blurred
6) Image flip
7) cropping and intensity transformation
8) Image sharpening
9) Gaussian Noise and filtering
10) Image Contrast
11) Speckle Noise and Filtering
20
351 Shearing
The image is resized with the scale factor Resized image is rotated with
angle(theta)Finally spatial transformation from control point pairs is implemented
For example
Scale factor 09
Theta10
Fig 32 Shearing Image
352 Image Scaling
It resizes the image with a scale factor and rotation is performed It rotates the
image by angle (degrees) in a counterclockwise direction around its center point To
rotate the image clockwise specify a negative value for angle It makes the output image
large enough to contain the entire rotated image It uses nearest neighbour interpolation
setting the values of pixels in Output image that are outside the rotated image to 0 (zero)
For example
Scale Factor07
Theta30
scaling Image
21
Fig 33 Scaling Image
353 Rotation
It rotates the image by angle degrees in a counterclockwise direction around its
center point To rotate the image clockwise specify a negative value for angle It makes
the output image large enough to contain the entire rotated image It uses nearest
neighbour interpolation setting the values of pixels in Output image that are outside the
rotated image to 0 (zero)
For Example
Theta180
Fig 34 Rotation Image
354 Colour Reduced Image
It creates an indexed image approximation of the RGB image in the array RGB by
dithering the colors in the colormap map The colormap cannot have more than 65536
resized and rotated image
Rotated image
22
colors
For Example
Indexed image with 32 Colors
Fig 35 Colour Reduced Image
355 Blur Image
The image is blurred by using N-D filtering of multidimensional images It filters
the multidimensional array of original image with the multidimensional filter The array
of original image can be logical or a nonsparse numeric array of any class and dimension
The result image has the same size and class as of original image
Fig 36 Blur Image
Color reduced image
50 100 150 200 250 300 350 400 450 500
50
100
150
200
250
300
350
400
450
500
Blurred image
23
356 Flipped Image
It flips the image upside down Flipping is used to invert the image
Fig 37 Flipped Image
357 Cropped Image
It creates an interactive crop image tool associated with the image displayed in the
current figure called the target image The crop image tool is a movable resizable
rectangle that you can position interactively using the mouse When the crop image tool
is active the pointer changes to cross hairs when it is moved over the target image
Using the mouse image to be cropped can be specified by clicking and dragging the
mouse The crop rectangle using the mouse can be moved or resized When sizing and
positioning of the crop rectangle is finished create the cropped image by double-clicking
the left mouse button or by choosing crop image from the context menu Image cropping
returns the cropped image
Flipped image
24
Fig 38 Cropped Image
358 Intensity Transformation Adjust
It maps the intensity values in grayscale image to new values in resultant image
such that 1 of data is saturated at low and high intensity of original image This
increases the contrast of the output image
Fig 39 Intensity Transformation Image
Cropped Image
Intensity Transformation
25
359 Sharpening
Input array values outside the bounds of the array are assumed to equal the nearest
array border value The image is sharpened by using N-D filtering of multidimensional
images It filters the multidimensional array of original image with the multidimensional
filter The array of original image can be logical or a nonsparse numeric array of any
class and dimension The result image has the same size and class as of original image
Fig 310 Sharpened Image
3510 Gaussian Noise and Median Filtering
It adds Gaussian noise to the images Gaussian white noise have constant mean
and variance The noise added image is filtered using Median Filtering Median filtering
is a nonlinear operation often used in image processing to reduce salt and pepper noise
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Sharpened Image
26
Fig 311 Gaussian Noise and Median Filter Image
3511 Histogram of contrast image
It enhances the contrast of images by transforming the values in an intensity
image or the values in the colormap of an indexed image so that the histogram of the
output image approximately matches a specified histogram
Fig 312 Contrast Image
Gaussian Noise
Median Filtering
Contrast Image
27
Fig 313 Histogram of Contrast Image
3512 Speckle noise and Median Filtering
It adds multiplicative noise to the image I using the equation J = I+nI where n is
uniformly distributed random noise with mean 0 and variance v The default for v is 004
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Fig 314 Speckle Noise and Median Filter Image
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Histogram of Contrast Image
0 50 100 150 200 250
Speckle Noise
Median Filtering
28
36 Proposed Algorithm
Step1 Enter two keys a private key and a public key through which the RSA algorithm is
performed
Step2These key are used for encrypting the secret data using chaotic algorithm with
threshold of 2
Step3The secret data is converted into binary format from ASCII code
Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied
Step5The encryption is carried out with the key generated randomly in binary(1times256)
Step6 The 2D logistic substitution and permutation are carried out
Step7The resulting binary sequence is added with the encrypted text in LSB
Step8The image is converted to DNA sequence and transmitted
Step9 Various Attacks have been applied on the resultant image
Step10The inverse process is carried out to retrieve the original image and data
Step11The Performance Metrics have been calculated
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
16
321 Key Generation
RSA involves a public key and a private key The public key can be known to
everyone and is used for encrypting messages Messages encrypted with the public key
can only be decrypted in a reasonable amount of time using the private key The keys for
the RSA algorithm are generated the following way To generate the two keys choose
two random large prime numbers p and q For maximum security choose p and q of
equal length Then randomly choose the encryption key e such that e and ( p minus1) (q minus1)
are relatively prime Finally use the extended Euclidean algorithm to compute the
decryption key d such that
d= e-1
mod ( (p-1) (q-1))
Note that d and n are also relatively prime The numbers e and K are the public
key the number d is the private key The two primes p and q are no longer needed They
should be discarded but never revealed
322 Encryption
Firstly receiver transmits her public key (n e) to sender and keeps the private key
secret If sender wishes to send message M to receiver Sender change the message M in
to integer m such that 0 le mltn Then sender computes the cipher text c corresponding to
Cequiv me
(mod n)
323 Decryption
Receiver can recover M from c by using private key exponent d via computing
M equiv cd
(mod n)
17
Algorithm
1Select any two prime numbers say (pq)
2Compute n=pq and also compute empty(119899)=(p-1)(q-1)
3Choose e such that 1ltelt empty(119899)
4Choose d such that (de)mod empty(119899)=1
5Public key is (en) and Private key is (dn)
6 If egt=2 then check i==1 if so return 1 else return 0
7In a iteration check for e(i)==1 if so take mod function of message with n
8Message is converted to cipher text in ASCII form with the key generated
9The cipher data in ASCII form is converted to binary form
33 2D Logistic Encryption
The chaotic system is a deterministic nonlinear system It possesses a varied
characteristics such as high sensitivity to initial conditions and system parameters
random-like behaviors and so forth Chaotic sequences produced by chaotic maps are
pseudo-random sequences their structures are very complex and difficult to be analyzed
and predicted In other words chaotic systems can improve the security of encryption
systems Thus it is advisable to encrypt digital image with chaotic systems There are
two chaotic maps one is logistic map and the other is 2D logistic map In the proposed
work 2D logistic map is used
Logistic map is an example for chaotic map and it is described as follows
x(n+1)=μ x(n)(1minusx(n))
μ is a positive constant sometimes known as the biotic potential gives the so-called
logistic map x(n) is series of values used to plot Logistic mapwhere μ isin [04] x(n) isin
(01) and n = 01 2hellip The research result shows that the system is in chaotic state
under the condition that 356994 lt μ le 4
18
2D logistic map is described in as follows
119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2
119910119894+1 = 1205832 lowast 119910119894(1 minus 119910119894) + 1205732((119909119894)2 + 119909119894 lowast 119910119894)
Where z(xy) is the logistic map 1205831 and 1205832 are positive constant sometimes known as the
biotic potential gives the so-called logistic map x be the position of images in x axis
and y be the position of images in y axis 1205731 and 1205732 are the correlation constantsWhen
275 lt μ1 le 34 275 lt μ2 le 345 015 lt 1205731le 021 and 013 lt 1205732 le 015 the system is in
chaotic state and can generate two chaotic sequences in the region (01] Due to the
system parameter γ1 and γ2 which have smaller value range we set γ1 = 017 and γ2 =
014 other parameters can be seen as secret keys
Algorithm
1A random key is generated in binary form ( 1times256) and it is stored in a array
2The random key generated is translated to map format using block processing (4times4)
3 The row and column wise transformation is carried out
4The key is now used to encrypt the cover image
52D logistic image undergoes substitution and permutation (column and row wise
shuffling is done)
34 DNA Sequence
A single DNA sequence is made up of four nucleic acid
bases A (adenine) C (cytosine) G (guanine) and T (thymine) where A and T are
complements and C and G are complements Let binary number 0 and 1 be
complements so 00 and 11 are complements and 01 and 10 are complements Thus we
can use these four bases A T G and C to encode 01 10 00 and 11 respectively The
encoding method still satisfies the Watson-Crick complement rule Usually each pixel
value of the 8 bit grey image can be expressed to 8 bits binary stream The binary stream
can be encoded to a DNA sequence whose length is 4 For example if the first pixel
19
value of the original image is 75 convert it into a binary stream [01001011] By using the
above DNA encoding rule to encode the stream we can get a DNA sequence [AGTC]
whereas we use A T G and C to express 01 10 00 and 11 respectively We can get a
binary sequence [01001011]
35 Attacks
The steganographic algorithm is used to embed secret messages into cover
image To obtain stego image while exchanging these stego-image through the public
communication channel various attacks have been made The are generally classified
into two types intentional or unintentional attacks Examples of unintentional attacks are
transmission errors lossy compression and changing the visual properties of the stego
document Intentional attacks on the other hand are deliberate attempts to distinguish
stego-objects from unmodified objects and thus detect the presence of covert
communication Attack methods generally exploit the fact that embedding information
usually changes the statistical properties of the objects compared to typical unmodified
objects In this proposed algorithm various attacks have been applied on the encrypted
image They are as follows
1)Shearing
2) Image Scaling
3) Image Rotating
4) Image color reduction
5) Image blurred
6) Image flip
7) cropping and intensity transformation
8) Image sharpening
9) Gaussian Noise and filtering
10) Image Contrast
11) Speckle Noise and Filtering
20
351 Shearing
The image is resized with the scale factor Resized image is rotated with
angle(theta)Finally spatial transformation from control point pairs is implemented
For example
Scale factor 09
Theta10
Fig 32 Shearing Image
352 Image Scaling
It resizes the image with a scale factor and rotation is performed It rotates the
image by angle (degrees) in a counterclockwise direction around its center point To
rotate the image clockwise specify a negative value for angle It makes the output image
large enough to contain the entire rotated image It uses nearest neighbour interpolation
setting the values of pixels in Output image that are outside the rotated image to 0 (zero)
For example
Scale Factor07
Theta30
scaling Image
21
Fig 33 Scaling Image
353 Rotation
It rotates the image by angle degrees in a counterclockwise direction around its
center point To rotate the image clockwise specify a negative value for angle It makes
the output image large enough to contain the entire rotated image It uses nearest
neighbour interpolation setting the values of pixels in Output image that are outside the
rotated image to 0 (zero)
For Example
Theta180
Fig 34 Rotation Image
354 Colour Reduced Image
It creates an indexed image approximation of the RGB image in the array RGB by
dithering the colors in the colormap map The colormap cannot have more than 65536
resized and rotated image
Rotated image
22
colors
For Example
Indexed image with 32 Colors
Fig 35 Colour Reduced Image
355 Blur Image
The image is blurred by using N-D filtering of multidimensional images It filters
the multidimensional array of original image with the multidimensional filter The array
of original image can be logical or a nonsparse numeric array of any class and dimension
The result image has the same size and class as of original image
Fig 36 Blur Image
Color reduced image
50 100 150 200 250 300 350 400 450 500
50
100
150
200
250
300
350
400
450
500
Blurred image
23
356 Flipped Image
It flips the image upside down Flipping is used to invert the image
Fig 37 Flipped Image
357 Cropped Image
It creates an interactive crop image tool associated with the image displayed in the
current figure called the target image The crop image tool is a movable resizable
rectangle that you can position interactively using the mouse When the crop image tool
is active the pointer changes to cross hairs when it is moved over the target image
Using the mouse image to be cropped can be specified by clicking and dragging the
mouse The crop rectangle using the mouse can be moved or resized When sizing and
positioning of the crop rectangle is finished create the cropped image by double-clicking
the left mouse button or by choosing crop image from the context menu Image cropping
returns the cropped image
Flipped image
24
Fig 38 Cropped Image
358 Intensity Transformation Adjust
It maps the intensity values in grayscale image to new values in resultant image
such that 1 of data is saturated at low and high intensity of original image This
increases the contrast of the output image
Fig 39 Intensity Transformation Image
Cropped Image
Intensity Transformation
25
359 Sharpening
Input array values outside the bounds of the array are assumed to equal the nearest
array border value The image is sharpened by using N-D filtering of multidimensional
images It filters the multidimensional array of original image with the multidimensional
filter The array of original image can be logical or a nonsparse numeric array of any
class and dimension The result image has the same size and class as of original image
Fig 310 Sharpened Image
3510 Gaussian Noise and Median Filtering
It adds Gaussian noise to the images Gaussian white noise have constant mean
and variance The noise added image is filtered using Median Filtering Median filtering
is a nonlinear operation often used in image processing to reduce salt and pepper noise
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Sharpened Image
26
Fig 311 Gaussian Noise and Median Filter Image
3511 Histogram of contrast image
It enhances the contrast of images by transforming the values in an intensity
image or the values in the colormap of an indexed image so that the histogram of the
output image approximately matches a specified histogram
Fig 312 Contrast Image
Gaussian Noise
Median Filtering
Contrast Image
27
Fig 313 Histogram of Contrast Image
3512 Speckle noise and Median Filtering
It adds multiplicative noise to the image I using the equation J = I+nI where n is
uniformly distributed random noise with mean 0 and variance v The default for v is 004
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Fig 314 Speckle Noise and Median Filter Image
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Histogram of Contrast Image
0 50 100 150 200 250
Speckle Noise
Median Filtering
28
36 Proposed Algorithm
Step1 Enter two keys a private key and a public key through which the RSA algorithm is
performed
Step2These key are used for encrypting the secret data using chaotic algorithm with
threshold of 2
Step3The secret data is converted into binary format from ASCII code
Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied
Step5The encryption is carried out with the key generated randomly in binary(1times256)
Step6 The 2D logistic substitution and permutation are carried out
Step7The resulting binary sequence is added with the encrypted text in LSB
Step8The image is converted to DNA sequence and transmitted
Step9 Various Attacks have been applied on the resultant image
Step10The inverse process is carried out to retrieve the original image and data
Step11The Performance Metrics have been calculated
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
17
Algorithm
1Select any two prime numbers say (pq)
2Compute n=pq and also compute empty(119899)=(p-1)(q-1)
3Choose e such that 1ltelt empty(119899)
4Choose d such that (de)mod empty(119899)=1
5Public key is (en) and Private key is (dn)
6 If egt=2 then check i==1 if so return 1 else return 0
7In a iteration check for e(i)==1 if so take mod function of message with n
8Message is converted to cipher text in ASCII form with the key generated
9The cipher data in ASCII form is converted to binary form
33 2D Logistic Encryption
The chaotic system is a deterministic nonlinear system It possesses a varied
characteristics such as high sensitivity to initial conditions and system parameters
random-like behaviors and so forth Chaotic sequences produced by chaotic maps are
pseudo-random sequences their structures are very complex and difficult to be analyzed
and predicted In other words chaotic systems can improve the security of encryption
systems Thus it is advisable to encrypt digital image with chaotic systems There are
two chaotic maps one is logistic map and the other is 2D logistic map In the proposed
work 2D logistic map is used
Logistic map is an example for chaotic map and it is described as follows
x(n+1)=μ x(n)(1minusx(n))
μ is a positive constant sometimes known as the biotic potential gives the so-called
logistic map x(n) is series of values used to plot Logistic mapwhere μ isin [04] x(n) isin
(01) and n = 01 2hellip The research result shows that the system is in chaotic state
under the condition that 356994 lt μ le 4
18
2D logistic map is described in as follows
119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2
119910119894+1 = 1205832 lowast 119910119894(1 minus 119910119894) + 1205732((119909119894)2 + 119909119894 lowast 119910119894)
Where z(xy) is the logistic map 1205831 and 1205832 are positive constant sometimes known as the
biotic potential gives the so-called logistic map x be the position of images in x axis
and y be the position of images in y axis 1205731 and 1205732 are the correlation constantsWhen
275 lt μ1 le 34 275 lt μ2 le 345 015 lt 1205731le 021 and 013 lt 1205732 le 015 the system is in
chaotic state and can generate two chaotic sequences in the region (01] Due to the
system parameter γ1 and γ2 which have smaller value range we set γ1 = 017 and γ2 =
014 other parameters can be seen as secret keys
Algorithm
1A random key is generated in binary form ( 1times256) and it is stored in a array
2The random key generated is translated to map format using block processing (4times4)
3 The row and column wise transformation is carried out
4The key is now used to encrypt the cover image
52D logistic image undergoes substitution and permutation (column and row wise
shuffling is done)
34 DNA Sequence
A single DNA sequence is made up of four nucleic acid
bases A (adenine) C (cytosine) G (guanine) and T (thymine) where A and T are
complements and C and G are complements Let binary number 0 and 1 be
complements so 00 and 11 are complements and 01 and 10 are complements Thus we
can use these four bases A T G and C to encode 01 10 00 and 11 respectively The
encoding method still satisfies the Watson-Crick complement rule Usually each pixel
value of the 8 bit grey image can be expressed to 8 bits binary stream The binary stream
can be encoded to a DNA sequence whose length is 4 For example if the first pixel
19
value of the original image is 75 convert it into a binary stream [01001011] By using the
above DNA encoding rule to encode the stream we can get a DNA sequence [AGTC]
whereas we use A T G and C to express 01 10 00 and 11 respectively We can get a
binary sequence [01001011]
35 Attacks
The steganographic algorithm is used to embed secret messages into cover
image To obtain stego image while exchanging these stego-image through the public
communication channel various attacks have been made The are generally classified
into two types intentional or unintentional attacks Examples of unintentional attacks are
transmission errors lossy compression and changing the visual properties of the stego
document Intentional attacks on the other hand are deliberate attempts to distinguish
stego-objects from unmodified objects and thus detect the presence of covert
communication Attack methods generally exploit the fact that embedding information
usually changes the statistical properties of the objects compared to typical unmodified
objects In this proposed algorithm various attacks have been applied on the encrypted
image They are as follows
1)Shearing
2) Image Scaling
3) Image Rotating
4) Image color reduction
5) Image blurred
6) Image flip
7) cropping and intensity transformation
8) Image sharpening
9) Gaussian Noise and filtering
10) Image Contrast
11) Speckle Noise and Filtering
20
351 Shearing
The image is resized with the scale factor Resized image is rotated with
angle(theta)Finally spatial transformation from control point pairs is implemented
For example
Scale factor 09
Theta10
Fig 32 Shearing Image
352 Image Scaling
It resizes the image with a scale factor and rotation is performed It rotates the
image by angle (degrees) in a counterclockwise direction around its center point To
rotate the image clockwise specify a negative value for angle It makes the output image
large enough to contain the entire rotated image It uses nearest neighbour interpolation
setting the values of pixels in Output image that are outside the rotated image to 0 (zero)
For example
Scale Factor07
Theta30
scaling Image
21
Fig 33 Scaling Image
353 Rotation
It rotates the image by angle degrees in a counterclockwise direction around its
center point To rotate the image clockwise specify a negative value for angle It makes
the output image large enough to contain the entire rotated image It uses nearest
neighbour interpolation setting the values of pixels in Output image that are outside the
rotated image to 0 (zero)
For Example
Theta180
Fig 34 Rotation Image
354 Colour Reduced Image
It creates an indexed image approximation of the RGB image in the array RGB by
dithering the colors in the colormap map The colormap cannot have more than 65536
resized and rotated image
Rotated image
22
colors
For Example
Indexed image with 32 Colors
Fig 35 Colour Reduced Image
355 Blur Image
The image is blurred by using N-D filtering of multidimensional images It filters
the multidimensional array of original image with the multidimensional filter The array
of original image can be logical or a nonsparse numeric array of any class and dimension
The result image has the same size and class as of original image
Fig 36 Blur Image
Color reduced image
50 100 150 200 250 300 350 400 450 500
50
100
150
200
250
300
350
400
450
500
Blurred image
23
356 Flipped Image
It flips the image upside down Flipping is used to invert the image
Fig 37 Flipped Image
357 Cropped Image
It creates an interactive crop image tool associated with the image displayed in the
current figure called the target image The crop image tool is a movable resizable
rectangle that you can position interactively using the mouse When the crop image tool
is active the pointer changes to cross hairs when it is moved over the target image
Using the mouse image to be cropped can be specified by clicking and dragging the
mouse The crop rectangle using the mouse can be moved or resized When sizing and
positioning of the crop rectangle is finished create the cropped image by double-clicking
the left mouse button or by choosing crop image from the context menu Image cropping
returns the cropped image
Flipped image
24
Fig 38 Cropped Image
358 Intensity Transformation Adjust
It maps the intensity values in grayscale image to new values in resultant image
such that 1 of data is saturated at low and high intensity of original image This
increases the contrast of the output image
Fig 39 Intensity Transformation Image
Cropped Image
Intensity Transformation
25
359 Sharpening
Input array values outside the bounds of the array are assumed to equal the nearest
array border value The image is sharpened by using N-D filtering of multidimensional
images It filters the multidimensional array of original image with the multidimensional
filter The array of original image can be logical or a nonsparse numeric array of any
class and dimension The result image has the same size and class as of original image
Fig 310 Sharpened Image
3510 Gaussian Noise and Median Filtering
It adds Gaussian noise to the images Gaussian white noise have constant mean
and variance The noise added image is filtered using Median Filtering Median filtering
is a nonlinear operation often used in image processing to reduce salt and pepper noise
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Sharpened Image
26
Fig 311 Gaussian Noise and Median Filter Image
3511 Histogram of contrast image
It enhances the contrast of images by transforming the values in an intensity
image or the values in the colormap of an indexed image so that the histogram of the
output image approximately matches a specified histogram
Fig 312 Contrast Image
Gaussian Noise
Median Filtering
Contrast Image
27
Fig 313 Histogram of Contrast Image
3512 Speckle noise and Median Filtering
It adds multiplicative noise to the image I using the equation J = I+nI where n is
uniformly distributed random noise with mean 0 and variance v The default for v is 004
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Fig 314 Speckle Noise and Median Filter Image
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Histogram of Contrast Image
0 50 100 150 200 250
Speckle Noise
Median Filtering
28
36 Proposed Algorithm
Step1 Enter two keys a private key and a public key through which the RSA algorithm is
performed
Step2These key are used for encrypting the secret data using chaotic algorithm with
threshold of 2
Step3The secret data is converted into binary format from ASCII code
Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied
Step5The encryption is carried out with the key generated randomly in binary(1times256)
Step6 The 2D logistic substitution and permutation are carried out
Step7The resulting binary sequence is added with the encrypted text in LSB
Step8The image is converted to DNA sequence and transmitted
Step9 Various Attacks have been applied on the resultant image
Step10The inverse process is carried out to retrieve the original image and data
Step11The Performance Metrics have been calculated
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
18
2D logistic map is described in as follows
119911(119909 119910) = 119909119894+1 = 1205831 lowast 119909119894(1 minus 119909119894) + 1205731(119910119894)2
119910119894+1 = 1205832 lowast 119910119894(1 minus 119910119894) + 1205732((119909119894)2 + 119909119894 lowast 119910119894)
Where z(xy) is the logistic map 1205831 and 1205832 are positive constant sometimes known as the
biotic potential gives the so-called logistic map x be the position of images in x axis
and y be the position of images in y axis 1205731 and 1205732 are the correlation constantsWhen
275 lt μ1 le 34 275 lt μ2 le 345 015 lt 1205731le 021 and 013 lt 1205732 le 015 the system is in
chaotic state and can generate two chaotic sequences in the region (01] Due to the
system parameter γ1 and γ2 which have smaller value range we set γ1 = 017 and γ2 =
014 other parameters can be seen as secret keys
Algorithm
1A random key is generated in binary form ( 1times256) and it is stored in a array
2The random key generated is translated to map format using block processing (4times4)
3 The row and column wise transformation is carried out
4The key is now used to encrypt the cover image
52D logistic image undergoes substitution and permutation (column and row wise
shuffling is done)
34 DNA Sequence
A single DNA sequence is made up of four nucleic acid
bases A (adenine) C (cytosine) G (guanine) and T (thymine) where A and T are
complements and C and G are complements Let binary number 0 and 1 be
complements so 00 and 11 are complements and 01 and 10 are complements Thus we
can use these four bases A T G and C to encode 01 10 00 and 11 respectively The
encoding method still satisfies the Watson-Crick complement rule Usually each pixel
value of the 8 bit grey image can be expressed to 8 bits binary stream The binary stream
can be encoded to a DNA sequence whose length is 4 For example if the first pixel
19
value of the original image is 75 convert it into a binary stream [01001011] By using the
above DNA encoding rule to encode the stream we can get a DNA sequence [AGTC]
whereas we use A T G and C to express 01 10 00 and 11 respectively We can get a
binary sequence [01001011]
35 Attacks
The steganographic algorithm is used to embed secret messages into cover
image To obtain stego image while exchanging these stego-image through the public
communication channel various attacks have been made The are generally classified
into two types intentional or unintentional attacks Examples of unintentional attacks are
transmission errors lossy compression and changing the visual properties of the stego
document Intentional attacks on the other hand are deliberate attempts to distinguish
stego-objects from unmodified objects and thus detect the presence of covert
communication Attack methods generally exploit the fact that embedding information
usually changes the statistical properties of the objects compared to typical unmodified
objects In this proposed algorithm various attacks have been applied on the encrypted
image They are as follows
1)Shearing
2) Image Scaling
3) Image Rotating
4) Image color reduction
5) Image blurred
6) Image flip
7) cropping and intensity transformation
8) Image sharpening
9) Gaussian Noise and filtering
10) Image Contrast
11) Speckle Noise and Filtering
20
351 Shearing
The image is resized with the scale factor Resized image is rotated with
angle(theta)Finally spatial transformation from control point pairs is implemented
For example
Scale factor 09
Theta10
Fig 32 Shearing Image
352 Image Scaling
It resizes the image with a scale factor and rotation is performed It rotates the
image by angle (degrees) in a counterclockwise direction around its center point To
rotate the image clockwise specify a negative value for angle It makes the output image
large enough to contain the entire rotated image It uses nearest neighbour interpolation
setting the values of pixels in Output image that are outside the rotated image to 0 (zero)
For example
Scale Factor07
Theta30
scaling Image
21
Fig 33 Scaling Image
353 Rotation
It rotates the image by angle degrees in a counterclockwise direction around its
center point To rotate the image clockwise specify a negative value for angle It makes
the output image large enough to contain the entire rotated image It uses nearest
neighbour interpolation setting the values of pixels in Output image that are outside the
rotated image to 0 (zero)
For Example
Theta180
Fig 34 Rotation Image
354 Colour Reduced Image
It creates an indexed image approximation of the RGB image in the array RGB by
dithering the colors in the colormap map The colormap cannot have more than 65536
resized and rotated image
Rotated image
22
colors
For Example
Indexed image with 32 Colors
Fig 35 Colour Reduced Image
355 Blur Image
The image is blurred by using N-D filtering of multidimensional images It filters
the multidimensional array of original image with the multidimensional filter The array
of original image can be logical or a nonsparse numeric array of any class and dimension
The result image has the same size and class as of original image
Fig 36 Blur Image
Color reduced image
50 100 150 200 250 300 350 400 450 500
50
100
150
200
250
300
350
400
450
500
Blurred image
23
356 Flipped Image
It flips the image upside down Flipping is used to invert the image
Fig 37 Flipped Image
357 Cropped Image
It creates an interactive crop image tool associated with the image displayed in the
current figure called the target image The crop image tool is a movable resizable
rectangle that you can position interactively using the mouse When the crop image tool
is active the pointer changes to cross hairs when it is moved over the target image
Using the mouse image to be cropped can be specified by clicking and dragging the
mouse The crop rectangle using the mouse can be moved or resized When sizing and
positioning of the crop rectangle is finished create the cropped image by double-clicking
the left mouse button or by choosing crop image from the context menu Image cropping
returns the cropped image
Flipped image
24
Fig 38 Cropped Image
358 Intensity Transformation Adjust
It maps the intensity values in grayscale image to new values in resultant image
such that 1 of data is saturated at low and high intensity of original image This
increases the contrast of the output image
Fig 39 Intensity Transformation Image
Cropped Image
Intensity Transformation
25
359 Sharpening
Input array values outside the bounds of the array are assumed to equal the nearest
array border value The image is sharpened by using N-D filtering of multidimensional
images It filters the multidimensional array of original image with the multidimensional
filter The array of original image can be logical or a nonsparse numeric array of any
class and dimension The result image has the same size and class as of original image
Fig 310 Sharpened Image
3510 Gaussian Noise and Median Filtering
It adds Gaussian noise to the images Gaussian white noise have constant mean
and variance The noise added image is filtered using Median Filtering Median filtering
is a nonlinear operation often used in image processing to reduce salt and pepper noise
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Sharpened Image
26
Fig 311 Gaussian Noise and Median Filter Image
3511 Histogram of contrast image
It enhances the contrast of images by transforming the values in an intensity
image or the values in the colormap of an indexed image so that the histogram of the
output image approximately matches a specified histogram
Fig 312 Contrast Image
Gaussian Noise
Median Filtering
Contrast Image
27
Fig 313 Histogram of Contrast Image
3512 Speckle noise and Median Filtering
It adds multiplicative noise to the image I using the equation J = I+nI where n is
uniformly distributed random noise with mean 0 and variance v The default for v is 004
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Fig 314 Speckle Noise and Median Filter Image
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Histogram of Contrast Image
0 50 100 150 200 250
Speckle Noise
Median Filtering
28
36 Proposed Algorithm
Step1 Enter two keys a private key and a public key through which the RSA algorithm is
performed
Step2These key are used for encrypting the secret data using chaotic algorithm with
threshold of 2
Step3The secret data is converted into binary format from ASCII code
Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied
Step5The encryption is carried out with the key generated randomly in binary(1times256)
Step6 The 2D logistic substitution and permutation are carried out
Step7The resulting binary sequence is added with the encrypted text in LSB
Step8The image is converted to DNA sequence and transmitted
Step9 Various Attacks have been applied on the resultant image
Step10The inverse process is carried out to retrieve the original image and data
Step11The Performance Metrics have been calculated
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
19
value of the original image is 75 convert it into a binary stream [01001011] By using the
above DNA encoding rule to encode the stream we can get a DNA sequence [AGTC]
whereas we use A T G and C to express 01 10 00 and 11 respectively We can get a
binary sequence [01001011]
35 Attacks
The steganographic algorithm is used to embed secret messages into cover
image To obtain stego image while exchanging these stego-image through the public
communication channel various attacks have been made The are generally classified
into two types intentional or unintentional attacks Examples of unintentional attacks are
transmission errors lossy compression and changing the visual properties of the stego
document Intentional attacks on the other hand are deliberate attempts to distinguish
stego-objects from unmodified objects and thus detect the presence of covert
communication Attack methods generally exploit the fact that embedding information
usually changes the statistical properties of the objects compared to typical unmodified
objects In this proposed algorithm various attacks have been applied on the encrypted
image They are as follows
1)Shearing
2) Image Scaling
3) Image Rotating
4) Image color reduction
5) Image blurred
6) Image flip
7) cropping and intensity transformation
8) Image sharpening
9) Gaussian Noise and filtering
10) Image Contrast
11) Speckle Noise and Filtering
20
351 Shearing
The image is resized with the scale factor Resized image is rotated with
angle(theta)Finally spatial transformation from control point pairs is implemented
For example
Scale factor 09
Theta10
Fig 32 Shearing Image
352 Image Scaling
It resizes the image with a scale factor and rotation is performed It rotates the
image by angle (degrees) in a counterclockwise direction around its center point To
rotate the image clockwise specify a negative value for angle It makes the output image
large enough to contain the entire rotated image It uses nearest neighbour interpolation
setting the values of pixels in Output image that are outside the rotated image to 0 (zero)
For example
Scale Factor07
Theta30
scaling Image
21
Fig 33 Scaling Image
353 Rotation
It rotates the image by angle degrees in a counterclockwise direction around its
center point To rotate the image clockwise specify a negative value for angle It makes
the output image large enough to contain the entire rotated image It uses nearest
neighbour interpolation setting the values of pixels in Output image that are outside the
rotated image to 0 (zero)
For Example
Theta180
Fig 34 Rotation Image
354 Colour Reduced Image
It creates an indexed image approximation of the RGB image in the array RGB by
dithering the colors in the colormap map The colormap cannot have more than 65536
resized and rotated image
Rotated image
22
colors
For Example
Indexed image with 32 Colors
Fig 35 Colour Reduced Image
355 Blur Image
The image is blurred by using N-D filtering of multidimensional images It filters
the multidimensional array of original image with the multidimensional filter The array
of original image can be logical or a nonsparse numeric array of any class and dimension
The result image has the same size and class as of original image
Fig 36 Blur Image
Color reduced image
50 100 150 200 250 300 350 400 450 500
50
100
150
200
250
300
350
400
450
500
Blurred image
23
356 Flipped Image
It flips the image upside down Flipping is used to invert the image
Fig 37 Flipped Image
357 Cropped Image
It creates an interactive crop image tool associated with the image displayed in the
current figure called the target image The crop image tool is a movable resizable
rectangle that you can position interactively using the mouse When the crop image tool
is active the pointer changes to cross hairs when it is moved over the target image
Using the mouse image to be cropped can be specified by clicking and dragging the
mouse The crop rectangle using the mouse can be moved or resized When sizing and
positioning of the crop rectangle is finished create the cropped image by double-clicking
the left mouse button or by choosing crop image from the context menu Image cropping
returns the cropped image
Flipped image
24
Fig 38 Cropped Image
358 Intensity Transformation Adjust
It maps the intensity values in grayscale image to new values in resultant image
such that 1 of data is saturated at low and high intensity of original image This
increases the contrast of the output image
Fig 39 Intensity Transformation Image
Cropped Image
Intensity Transformation
25
359 Sharpening
Input array values outside the bounds of the array are assumed to equal the nearest
array border value The image is sharpened by using N-D filtering of multidimensional
images It filters the multidimensional array of original image with the multidimensional
filter The array of original image can be logical or a nonsparse numeric array of any
class and dimension The result image has the same size and class as of original image
Fig 310 Sharpened Image
3510 Gaussian Noise and Median Filtering
It adds Gaussian noise to the images Gaussian white noise have constant mean
and variance The noise added image is filtered using Median Filtering Median filtering
is a nonlinear operation often used in image processing to reduce salt and pepper noise
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Sharpened Image
26
Fig 311 Gaussian Noise and Median Filter Image
3511 Histogram of contrast image
It enhances the contrast of images by transforming the values in an intensity
image or the values in the colormap of an indexed image so that the histogram of the
output image approximately matches a specified histogram
Fig 312 Contrast Image
Gaussian Noise
Median Filtering
Contrast Image
27
Fig 313 Histogram of Contrast Image
3512 Speckle noise and Median Filtering
It adds multiplicative noise to the image I using the equation J = I+nI where n is
uniformly distributed random noise with mean 0 and variance v The default for v is 004
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Fig 314 Speckle Noise and Median Filter Image
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Histogram of Contrast Image
0 50 100 150 200 250
Speckle Noise
Median Filtering
28
36 Proposed Algorithm
Step1 Enter two keys a private key and a public key through which the RSA algorithm is
performed
Step2These key are used for encrypting the secret data using chaotic algorithm with
threshold of 2
Step3The secret data is converted into binary format from ASCII code
Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied
Step5The encryption is carried out with the key generated randomly in binary(1times256)
Step6 The 2D logistic substitution and permutation are carried out
Step7The resulting binary sequence is added with the encrypted text in LSB
Step8The image is converted to DNA sequence and transmitted
Step9 Various Attacks have been applied on the resultant image
Step10The inverse process is carried out to retrieve the original image and data
Step11The Performance Metrics have been calculated
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
20
351 Shearing
The image is resized with the scale factor Resized image is rotated with
angle(theta)Finally spatial transformation from control point pairs is implemented
For example
Scale factor 09
Theta10
Fig 32 Shearing Image
352 Image Scaling
It resizes the image with a scale factor and rotation is performed It rotates the
image by angle (degrees) in a counterclockwise direction around its center point To
rotate the image clockwise specify a negative value for angle It makes the output image
large enough to contain the entire rotated image It uses nearest neighbour interpolation
setting the values of pixels in Output image that are outside the rotated image to 0 (zero)
For example
Scale Factor07
Theta30
scaling Image
21
Fig 33 Scaling Image
353 Rotation
It rotates the image by angle degrees in a counterclockwise direction around its
center point To rotate the image clockwise specify a negative value for angle It makes
the output image large enough to contain the entire rotated image It uses nearest
neighbour interpolation setting the values of pixels in Output image that are outside the
rotated image to 0 (zero)
For Example
Theta180
Fig 34 Rotation Image
354 Colour Reduced Image
It creates an indexed image approximation of the RGB image in the array RGB by
dithering the colors in the colormap map The colormap cannot have more than 65536
resized and rotated image
Rotated image
22
colors
For Example
Indexed image with 32 Colors
Fig 35 Colour Reduced Image
355 Blur Image
The image is blurred by using N-D filtering of multidimensional images It filters
the multidimensional array of original image with the multidimensional filter The array
of original image can be logical or a nonsparse numeric array of any class and dimension
The result image has the same size and class as of original image
Fig 36 Blur Image
Color reduced image
50 100 150 200 250 300 350 400 450 500
50
100
150
200
250
300
350
400
450
500
Blurred image
23
356 Flipped Image
It flips the image upside down Flipping is used to invert the image
Fig 37 Flipped Image
357 Cropped Image
It creates an interactive crop image tool associated with the image displayed in the
current figure called the target image The crop image tool is a movable resizable
rectangle that you can position interactively using the mouse When the crop image tool
is active the pointer changes to cross hairs when it is moved over the target image
Using the mouse image to be cropped can be specified by clicking and dragging the
mouse The crop rectangle using the mouse can be moved or resized When sizing and
positioning of the crop rectangle is finished create the cropped image by double-clicking
the left mouse button or by choosing crop image from the context menu Image cropping
returns the cropped image
Flipped image
24
Fig 38 Cropped Image
358 Intensity Transformation Adjust
It maps the intensity values in grayscale image to new values in resultant image
such that 1 of data is saturated at low and high intensity of original image This
increases the contrast of the output image
Fig 39 Intensity Transformation Image
Cropped Image
Intensity Transformation
25
359 Sharpening
Input array values outside the bounds of the array are assumed to equal the nearest
array border value The image is sharpened by using N-D filtering of multidimensional
images It filters the multidimensional array of original image with the multidimensional
filter The array of original image can be logical or a nonsparse numeric array of any
class and dimension The result image has the same size and class as of original image
Fig 310 Sharpened Image
3510 Gaussian Noise and Median Filtering
It adds Gaussian noise to the images Gaussian white noise have constant mean
and variance The noise added image is filtered using Median Filtering Median filtering
is a nonlinear operation often used in image processing to reduce salt and pepper noise
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Sharpened Image
26
Fig 311 Gaussian Noise and Median Filter Image
3511 Histogram of contrast image
It enhances the contrast of images by transforming the values in an intensity
image or the values in the colormap of an indexed image so that the histogram of the
output image approximately matches a specified histogram
Fig 312 Contrast Image
Gaussian Noise
Median Filtering
Contrast Image
27
Fig 313 Histogram of Contrast Image
3512 Speckle noise and Median Filtering
It adds multiplicative noise to the image I using the equation J = I+nI where n is
uniformly distributed random noise with mean 0 and variance v The default for v is 004
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Fig 314 Speckle Noise and Median Filter Image
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Histogram of Contrast Image
0 50 100 150 200 250
Speckle Noise
Median Filtering
28
36 Proposed Algorithm
Step1 Enter two keys a private key and a public key through which the RSA algorithm is
performed
Step2These key are used for encrypting the secret data using chaotic algorithm with
threshold of 2
Step3The secret data is converted into binary format from ASCII code
Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied
Step5The encryption is carried out with the key generated randomly in binary(1times256)
Step6 The 2D logistic substitution and permutation are carried out
Step7The resulting binary sequence is added with the encrypted text in LSB
Step8The image is converted to DNA sequence and transmitted
Step9 Various Attacks have been applied on the resultant image
Step10The inverse process is carried out to retrieve the original image and data
Step11The Performance Metrics have been calculated
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
21
Fig 33 Scaling Image
353 Rotation
It rotates the image by angle degrees in a counterclockwise direction around its
center point To rotate the image clockwise specify a negative value for angle It makes
the output image large enough to contain the entire rotated image It uses nearest
neighbour interpolation setting the values of pixels in Output image that are outside the
rotated image to 0 (zero)
For Example
Theta180
Fig 34 Rotation Image
354 Colour Reduced Image
It creates an indexed image approximation of the RGB image in the array RGB by
dithering the colors in the colormap map The colormap cannot have more than 65536
resized and rotated image
Rotated image
22
colors
For Example
Indexed image with 32 Colors
Fig 35 Colour Reduced Image
355 Blur Image
The image is blurred by using N-D filtering of multidimensional images It filters
the multidimensional array of original image with the multidimensional filter The array
of original image can be logical or a nonsparse numeric array of any class and dimension
The result image has the same size and class as of original image
Fig 36 Blur Image
Color reduced image
50 100 150 200 250 300 350 400 450 500
50
100
150
200
250
300
350
400
450
500
Blurred image
23
356 Flipped Image
It flips the image upside down Flipping is used to invert the image
Fig 37 Flipped Image
357 Cropped Image
It creates an interactive crop image tool associated with the image displayed in the
current figure called the target image The crop image tool is a movable resizable
rectangle that you can position interactively using the mouse When the crop image tool
is active the pointer changes to cross hairs when it is moved over the target image
Using the mouse image to be cropped can be specified by clicking and dragging the
mouse The crop rectangle using the mouse can be moved or resized When sizing and
positioning of the crop rectangle is finished create the cropped image by double-clicking
the left mouse button or by choosing crop image from the context menu Image cropping
returns the cropped image
Flipped image
24
Fig 38 Cropped Image
358 Intensity Transformation Adjust
It maps the intensity values in grayscale image to new values in resultant image
such that 1 of data is saturated at low and high intensity of original image This
increases the contrast of the output image
Fig 39 Intensity Transformation Image
Cropped Image
Intensity Transformation
25
359 Sharpening
Input array values outside the bounds of the array are assumed to equal the nearest
array border value The image is sharpened by using N-D filtering of multidimensional
images It filters the multidimensional array of original image with the multidimensional
filter The array of original image can be logical or a nonsparse numeric array of any
class and dimension The result image has the same size and class as of original image
Fig 310 Sharpened Image
3510 Gaussian Noise and Median Filtering
It adds Gaussian noise to the images Gaussian white noise have constant mean
and variance The noise added image is filtered using Median Filtering Median filtering
is a nonlinear operation often used in image processing to reduce salt and pepper noise
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Sharpened Image
26
Fig 311 Gaussian Noise and Median Filter Image
3511 Histogram of contrast image
It enhances the contrast of images by transforming the values in an intensity
image or the values in the colormap of an indexed image so that the histogram of the
output image approximately matches a specified histogram
Fig 312 Contrast Image
Gaussian Noise
Median Filtering
Contrast Image
27
Fig 313 Histogram of Contrast Image
3512 Speckle noise and Median Filtering
It adds multiplicative noise to the image I using the equation J = I+nI where n is
uniformly distributed random noise with mean 0 and variance v The default for v is 004
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Fig 314 Speckle Noise and Median Filter Image
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Histogram of Contrast Image
0 50 100 150 200 250
Speckle Noise
Median Filtering
28
36 Proposed Algorithm
Step1 Enter two keys a private key and a public key through which the RSA algorithm is
performed
Step2These key are used for encrypting the secret data using chaotic algorithm with
threshold of 2
Step3The secret data is converted into binary format from ASCII code
Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied
Step5The encryption is carried out with the key generated randomly in binary(1times256)
Step6 The 2D logistic substitution and permutation are carried out
Step7The resulting binary sequence is added with the encrypted text in LSB
Step8The image is converted to DNA sequence and transmitted
Step9 Various Attacks have been applied on the resultant image
Step10The inverse process is carried out to retrieve the original image and data
Step11The Performance Metrics have been calculated
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
22
colors
For Example
Indexed image with 32 Colors
Fig 35 Colour Reduced Image
355 Blur Image
The image is blurred by using N-D filtering of multidimensional images It filters
the multidimensional array of original image with the multidimensional filter The array
of original image can be logical or a nonsparse numeric array of any class and dimension
The result image has the same size and class as of original image
Fig 36 Blur Image
Color reduced image
50 100 150 200 250 300 350 400 450 500
50
100
150
200
250
300
350
400
450
500
Blurred image
23
356 Flipped Image
It flips the image upside down Flipping is used to invert the image
Fig 37 Flipped Image
357 Cropped Image
It creates an interactive crop image tool associated with the image displayed in the
current figure called the target image The crop image tool is a movable resizable
rectangle that you can position interactively using the mouse When the crop image tool
is active the pointer changes to cross hairs when it is moved over the target image
Using the mouse image to be cropped can be specified by clicking and dragging the
mouse The crop rectangle using the mouse can be moved or resized When sizing and
positioning of the crop rectangle is finished create the cropped image by double-clicking
the left mouse button or by choosing crop image from the context menu Image cropping
returns the cropped image
Flipped image
24
Fig 38 Cropped Image
358 Intensity Transformation Adjust
It maps the intensity values in grayscale image to new values in resultant image
such that 1 of data is saturated at low and high intensity of original image This
increases the contrast of the output image
Fig 39 Intensity Transformation Image
Cropped Image
Intensity Transformation
25
359 Sharpening
Input array values outside the bounds of the array are assumed to equal the nearest
array border value The image is sharpened by using N-D filtering of multidimensional
images It filters the multidimensional array of original image with the multidimensional
filter The array of original image can be logical or a nonsparse numeric array of any
class and dimension The result image has the same size and class as of original image
Fig 310 Sharpened Image
3510 Gaussian Noise and Median Filtering
It adds Gaussian noise to the images Gaussian white noise have constant mean
and variance The noise added image is filtered using Median Filtering Median filtering
is a nonlinear operation often used in image processing to reduce salt and pepper noise
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Sharpened Image
26
Fig 311 Gaussian Noise and Median Filter Image
3511 Histogram of contrast image
It enhances the contrast of images by transforming the values in an intensity
image or the values in the colormap of an indexed image so that the histogram of the
output image approximately matches a specified histogram
Fig 312 Contrast Image
Gaussian Noise
Median Filtering
Contrast Image
27
Fig 313 Histogram of Contrast Image
3512 Speckle noise and Median Filtering
It adds multiplicative noise to the image I using the equation J = I+nI where n is
uniformly distributed random noise with mean 0 and variance v The default for v is 004
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Fig 314 Speckle Noise and Median Filter Image
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Histogram of Contrast Image
0 50 100 150 200 250
Speckle Noise
Median Filtering
28
36 Proposed Algorithm
Step1 Enter two keys a private key and a public key through which the RSA algorithm is
performed
Step2These key are used for encrypting the secret data using chaotic algorithm with
threshold of 2
Step3The secret data is converted into binary format from ASCII code
Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied
Step5The encryption is carried out with the key generated randomly in binary(1times256)
Step6 The 2D logistic substitution and permutation are carried out
Step7The resulting binary sequence is added with the encrypted text in LSB
Step8The image is converted to DNA sequence and transmitted
Step9 Various Attacks have been applied on the resultant image
Step10The inverse process is carried out to retrieve the original image and data
Step11The Performance Metrics have been calculated
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
23
356 Flipped Image
It flips the image upside down Flipping is used to invert the image
Fig 37 Flipped Image
357 Cropped Image
It creates an interactive crop image tool associated with the image displayed in the
current figure called the target image The crop image tool is a movable resizable
rectangle that you can position interactively using the mouse When the crop image tool
is active the pointer changes to cross hairs when it is moved over the target image
Using the mouse image to be cropped can be specified by clicking and dragging the
mouse The crop rectangle using the mouse can be moved or resized When sizing and
positioning of the crop rectangle is finished create the cropped image by double-clicking
the left mouse button or by choosing crop image from the context menu Image cropping
returns the cropped image
Flipped image
24
Fig 38 Cropped Image
358 Intensity Transformation Adjust
It maps the intensity values in grayscale image to new values in resultant image
such that 1 of data is saturated at low and high intensity of original image This
increases the contrast of the output image
Fig 39 Intensity Transformation Image
Cropped Image
Intensity Transformation
25
359 Sharpening
Input array values outside the bounds of the array are assumed to equal the nearest
array border value The image is sharpened by using N-D filtering of multidimensional
images It filters the multidimensional array of original image with the multidimensional
filter The array of original image can be logical or a nonsparse numeric array of any
class and dimension The result image has the same size and class as of original image
Fig 310 Sharpened Image
3510 Gaussian Noise and Median Filtering
It adds Gaussian noise to the images Gaussian white noise have constant mean
and variance The noise added image is filtered using Median Filtering Median filtering
is a nonlinear operation often used in image processing to reduce salt and pepper noise
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Sharpened Image
26
Fig 311 Gaussian Noise and Median Filter Image
3511 Histogram of contrast image
It enhances the contrast of images by transforming the values in an intensity
image or the values in the colormap of an indexed image so that the histogram of the
output image approximately matches a specified histogram
Fig 312 Contrast Image
Gaussian Noise
Median Filtering
Contrast Image
27
Fig 313 Histogram of Contrast Image
3512 Speckle noise and Median Filtering
It adds multiplicative noise to the image I using the equation J = I+nI where n is
uniformly distributed random noise with mean 0 and variance v The default for v is 004
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Fig 314 Speckle Noise and Median Filter Image
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Histogram of Contrast Image
0 50 100 150 200 250
Speckle Noise
Median Filtering
28
36 Proposed Algorithm
Step1 Enter two keys a private key and a public key through which the RSA algorithm is
performed
Step2These key are used for encrypting the secret data using chaotic algorithm with
threshold of 2
Step3The secret data is converted into binary format from ASCII code
Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied
Step5The encryption is carried out with the key generated randomly in binary(1times256)
Step6 The 2D logistic substitution and permutation are carried out
Step7The resulting binary sequence is added with the encrypted text in LSB
Step8The image is converted to DNA sequence and transmitted
Step9 Various Attacks have been applied on the resultant image
Step10The inverse process is carried out to retrieve the original image and data
Step11The Performance Metrics have been calculated
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
24
Fig 38 Cropped Image
358 Intensity Transformation Adjust
It maps the intensity values in grayscale image to new values in resultant image
such that 1 of data is saturated at low and high intensity of original image This
increases the contrast of the output image
Fig 39 Intensity Transformation Image
Cropped Image
Intensity Transformation
25
359 Sharpening
Input array values outside the bounds of the array are assumed to equal the nearest
array border value The image is sharpened by using N-D filtering of multidimensional
images It filters the multidimensional array of original image with the multidimensional
filter The array of original image can be logical or a nonsparse numeric array of any
class and dimension The result image has the same size and class as of original image
Fig 310 Sharpened Image
3510 Gaussian Noise and Median Filtering
It adds Gaussian noise to the images Gaussian white noise have constant mean
and variance The noise added image is filtered using Median Filtering Median filtering
is a nonlinear operation often used in image processing to reduce salt and pepper noise
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Sharpened Image
26
Fig 311 Gaussian Noise and Median Filter Image
3511 Histogram of contrast image
It enhances the contrast of images by transforming the values in an intensity
image or the values in the colormap of an indexed image so that the histogram of the
output image approximately matches a specified histogram
Fig 312 Contrast Image
Gaussian Noise
Median Filtering
Contrast Image
27
Fig 313 Histogram of Contrast Image
3512 Speckle noise and Median Filtering
It adds multiplicative noise to the image I using the equation J = I+nI where n is
uniformly distributed random noise with mean 0 and variance v The default for v is 004
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Fig 314 Speckle Noise and Median Filter Image
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Histogram of Contrast Image
0 50 100 150 200 250
Speckle Noise
Median Filtering
28
36 Proposed Algorithm
Step1 Enter two keys a private key and a public key through which the RSA algorithm is
performed
Step2These key are used for encrypting the secret data using chaotic algorithm with
threshold of 2
Step3The secret data is converted into binary format from ASCII code
Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied
Step5The encryption is carried out with the key generated randomly in binary(1times256)
Step6 The 2D logistic substitution and permutation are carried out
Step7The resulting binary sequence is added with the encrypted text in LSB
Step8The image is converted to DNA sequence and transmitted
Step9 Various Attacks have been applied on the resultant image
Step10The inverse process is carried out to retrieve the original image and data
Step11The Performance Metrics have been calculated
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
25
359 Sharpening
Input array values outside the bounds of the array are assumed to equal the nearest
array border value The image is sharpened by using N-D filtering of multidimensional
images It filters the multidimensional array of original image with the multidimensional
filter The array of original image can be logical or a nonsparse numeric array of any
class and dimension The result image has the same size and class as of original image
Fig 310 Sharpened Image
3510 Gaussian Noise and Median Filtering
It adds Gaussian noise to the images Gaussian white noise have constant mean
and variance The noise added image is filtered using Median Filtering Median filtering
is a nonlinear operation often used in image processing to reduce salt and pepper noise
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Sharpened Image
26
Fig 311 Gaussian Noise and Median Filter Image
3511 Histogram of contrast image
It enhances the contrast of images by transforming the values in an intensity
image or the values in the colormap of an indexed image so that the histogram of the
output image approximately matches a specified histogram
Fig 312 Contrast Image
Gaussian Noise
Median Filtering
Contrast Image
27
Fig 313 Histogram of Contrast Image
3512 Speckle noise and Median Filtering
It adds multiplicative noise to the image I using the equation J = I+nI where n is
uniformly distributed random noise with mean 0 and variance v The default for v is 004
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Fig 314 Speckle Noise and Median Filter Image
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Histogram of Contrast Image
0 50 100 150 200 250
Speckle Noise
Median Filtering
28
36 Proposed Algorithm
Step1 Enter two keys a private key and a public key through which the RSA algorithm is
performed
Step2These key are used for encrypting the secret data using chaotic algorithm with
threshold of 2
Step3The secret data is converted into binary format from ASCII code
Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied
Step5The encryption is carried out with the key generated randomly in binary(1times256)
Step6 The 2D logistic substitution and permutation are carried out
Step7The resulting binary sequence is added with the encrypted text in LSB
Step8The image is converted to DNA sequence and transmitted
Step9 Various Attacks have been applied on the resultant image
Step10The inverse process is carried out to retrieve the original image and data
Step11The Performance Metrics have been calculated
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
26
Fig 311 Gaussian Noise and Median Filter Image
3511 Histogram of contrast image
It enhances the contrast of images by transforming the values in an intensity
image or the values in the colormap of an indexed image so that the histogram of the
output image approximately matches a specified histogram
Fig 312 Contrast Image
Gaussian Noise
Median Filtering
Contrast Image
27
Fig 313 Histogram of Contrast Image
3512 Speckle noise and Median Filtering
It adds multiplicative noise to the image I using the equation J = I+nI where n is
uniformly distributed random noise with mean 0 and variance v The default for v is 004
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Fig 314 Speckle Noise and Median Filter Image
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Histogram of Contrast Image
0 50 100 150 200 250
Speckle Noise
Median Filtering
28
36 Proposed Algorithm
Step1 Enter two keys a private key and a public key through which the RSA algorithm is
performed
Step2These key are used for encrypting the secret data using chaotic algorithm with
threshold of 2
Step3The secret data is converted into binary format from ASCII code
Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied
Step5The encryption is carried out with the key generated randomly in binary(1times256)
Step6 The 2D logistic substitution and permutation are carried out
Step7The resulting binary sequence is added with the encrypted text in LSB
Step8The image is converted to DNA sequence and transmitted
Step9 Various Attacks have been applied on the resultant image
Step10The inverse process is carried out to retrieve the original image and data
Step11The Performance Metrics have been calculated
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
27
Fig 313 Histogram of Contrast Image
3512 Speckle noise and Median Filtering
It adds multiplicative noise to the image I using the equation J = I+nI where n is
uniformly distributed random noise with mean 0 and variance v The default for v is 004
A median filter is more effective than convolution when the goal is to simultaneously
reduce noise and preserve edges Each output pixel contains the median value in the m-
by-n neighborhood around the corresponding pixel in the input image Median filter pads
the image with 0s on the edges so the median values for the points within [m n]2 of the
edges might appear distorted
Fig 314 Speckle Noise and Median Filter Image
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Histogram of Contrast Image
0 50 100 150 200 250
Speckle Noise
Median Filtering
28
36 Proposed Algorithm
Step1 Enter two keys a private key and a public key through which the RSA algorithm is
performed
Step2These key are used for encrypting the secret data using chaotic algorithm with
threshold of 2
Step3The secret data is converted into binary format from ASCII code
Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied
Step5The encryption is carried out with the key generated randomly in binary(1times256)
Step6 The 2D logistic substitution and permutation are carried out
Step7The resulting binary sequence is added with the encrypted text in LSB
Step8The image is converted to DNA sequence and transmitted
Step9 Various Attacks have been applied on the resultant image
Step10The inverse process is carried out to retrieve the original image and data
Step11The Performance Metrics have been calculated
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
28
36 Proposed Algorithm
Step1 Enter two keys a private key and a public key through which the RSA algorithm is
performed
Step2These key are used for encrypting the secret data using chaotic algorithm with
threshold of 2
Step3The secret data is converted into binary format from ASCII code
Step4 In the cover image every pixel intensity is taken 2D logistic encryption is applied
Step5The encryption is carried out with the key generated randomly in binary(1times256)
Step6 The 2D logistic substitution and permutation are carried out
Step7The resulting binary sequence is added with the encrypted text in LSB
Step8The image is converted to DNA sequence and transmitted
Step9 Various Attacks have been applied on the resultant image
Step10The inverse process is carried out to retrieve the original image and data
Step11The Performance Metrics have been calculated
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
29
CHAPTER 4
RESULTS AND DISCUSSIONS
The performance metrics of the proposed method have been evaluated
The various performance metrics are
(i) Peak Signal to Noise Ratio (PSNR)
(ii) Mean Square Error (MSE)
(iii) Structural content (SC)
(iv) Average Difference(AD)
(v) Normalized Cross Correlation(NCC)
(vi) Laplacian Mean Squared Error(LMSE)
(vii) Normalized Absolute Error(NAE)
(viii) Maximum Difference (MD)
Peak Signal to Noise Ratio (PSNR) is defined as
PSNR = 10 log10
1
0
21
0
1
0
1
0
2
)()(
255
m
i
n
j
m
i
n
j
jiIjiI (41)
Where I(i j) and I‟(i j) are the corresponding cover image and Stego image pixel
intensities
The Mean square error (MSE) is the measure of average of the square of the errors that is
the difference between the expected value and the actual value
MSE = 1
MNsum sum I(i j) minus Iprime(i j)
Nminus1
0
Mminus1
0
(42)
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
30
The Normalized Cross Correlation is a measure of similarity of two series as a function
of the lag of one relative to the other
NCC = --------------------------------------------------(43)
Where f(xy) is a original image t(xy) is a reconstructed image 119891 is original image mean
and 119905 is the reconstructed image mean and 120590119891 and 120590119905 is the standard deviation of original
and reconstructed image Absolute difference is measure of finding difference between
original image and the reconstructed image in pixel by pixel manner
AD = -----------------------------------------------------(44)
Where M and N are dimension of row and column respectively
Maximum Difference is the measure of maximum of difference between original and
recovered image
MD = max(original image ndash recovered image) (45)
Let us take F to be original image and be the recovered image
The Structural Content is used for measuring the similarity between two images
(46)
The Normalized Absolute Error is quantity used to measure how close forecasts or
predictions are to the eventual outcomes
(47)
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
31
The Laplacian Mean Square Error performs well in discriminating the images with
different quality
(48)
where
Figure 41 Gray Scale Cover Images of size 256times256 (a) Barbara (b) Boat
(c) Butterfly (d) Charlie Chaplain (e) Lena
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
32
Figure 42 Input Image and 2D Logistic Encrypted Image
CTTGGCGAGCAAGATGGCATCTTAGGTTGGCTGAGTCTGCGACCCTCGCTGCGAACGAATCTCC
CTTACTGCGAATGACTGAATCTCGCTGCCGTCGAAAGACCCGTGGAGCCTGTCTCTGAATGCTTG
AGAGCGCACCTACCCACTAAGAAACGAAGCTATACATGCATCGAGTGACGGAATGACAAACTAAT
GAATTAAGTCAGCGAGCTAGCAACTTCTCACGTCCTGTGCCGCGGTCTAAACAAAGAAATAAATA
TATCGAGTTACTGACGTACCTACCCACGAACCTACCTACGAACTAACTCACGAACCGACGAAGAA
ATGTGCCCGATAGAGAGAACCTCTCTGTCTCCCTGAGAAGGACCCTGTCTTCGACGCTAGGAAC
GAGACCTCCCGTACGTAGTACACTGGCCTATCTTGGTTGCGTGTCGTCGGATGATTCTTCGAGGG
AAACTTCCTATGAAGCTGTGAGTCTAGCTCGGATCGCTTGACGCTTGGCAGCTCAGCCTGACCCG
TTGACTCGAGCGGAGAATCTTGGACCGACAGCGGCTTAGATACGCCCTCTCTGCCGAGCTCAGA
GAGACTCGTGGAAAGACGGAACGACTGACGGATCGAGTCTTTGACGGATCGAGGGCGCCTCTGA
AACTCCCTGCCTATCTAACTCAGCAAGACTCTTCCTCTGCAACTGGCTTCCTTACTGAGAAACTCT
CTCACTGACGTTCGGGGAACCTCCGAATCCGGCCTACGTTCTTACTTCCGGTCGTGCGTCATCAA
TCCCCATTAATTGGGGGATGAATCTCCGATACGTTCGGCCTCCCGATGAGAGAAACGGGCGTTCG
CGCCACCCGCCGCTCGTTCTATGATTCTAACTAGCGCAGCTACTTTCTCTCTACCTCAGAGGCTTG
CTCACTCCCGCCCGAGCCGTCGACCCTCCCTCCGTGCGAACTCTCTAGCGCGCCCTCGTTCGTC
CGTCCGGACTGGCTCTGAATCGTGCTGCCGCGGAAACGATCTATCGCCCCCTCTGCCTTGGAAG
GGAGCGCGGGAAGGTAGACCGCCGGGCAGCATCTCACTTCGAGAGCCAGAAACGCTGACAGAA
Figure 43 DNA Sequence
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
33
Figure 44 Recovered Image
Figure 45 Recovered Text
Table 41Performance Metric Calculation
Image
PSNR MSE AD LMSE NAE MD NCC SC
Barbara 4572 00174 -01054 00076 00064 233 09248 08257
Boat
4491 00209 -00898 00001 00054 230 08161 09811
Butterfly 4584 00163 -01079 00002 00061 207 09702 07058
Charlie
Chaplin
4780 00107 -04982 00001 00117 246 09432 08709
Lena 4724 00122 -03137 00009 00081 218 09595 08570
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
34
Various attacks have been applied on the encrypted image The performance
metrics of the proposed method have been evaluated between the original and attack
based recovered image In Table 42 shows the Normalized Cross Correlation (NCC)
between the original and the recovered image Bit Error Rate (BER) is calculated
between original and recovered text
S No Attacks on Barbara Image NCC BER
1 Shearing 09043 00057
2 Image Scaling 09037 00043
3 Image Rotating 09031 00047
4 Image color reduction 09046 00051
5 Image blurred 09006 00035
6 Image flip 09069 00044
7 cropping and intensity transformation 09099 00046
8 Image sharpening 09071 00039
9 Gaussian Noise and filtering 09040 00053
10 Image Contrast 09070 00055
11 Speckle Noise and Filtering 09068 00048
Table 42Performance Metric Calculation between original and recovered Barbara
image
Inference
1 As the NCC values are greater than 090 for all types of attacks the proposed
algorithm is reversible
2 As the BER is less than 0006 the proposed algorithm is robust against various
attacks
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
35
CHAPTER 5
CONCLUSION AND FUTUREWORK
51 CONCLUSION
In this proposed work the 2D-logistic encryption algorithm is used for encrypting the
image and RSA based chaos encryption is used to encrypt the data This proposed scheme
ensures the data security with higher success rates and provides high data embedding
capacity This method provides high security for data that is embedded in the cover image
The cover image is 2D logistic encrypted to embed the data into the cover image to get
better results The image is converted into DNA Sequence to provide additional level of
security Attacks have been applied to the resultant image Peak Signal to Noise Ratio
(PSNR) Structural Content (SC) Normalized Cross Correlation(NCC) Maximum
Difference (MD)Average Difference(AD) and Laplacian Mean Square Error(LMSE) have
been used to measure the quality of the extracted image The Normalized Cross
Correlation has been calculated between original and recovered image As the NCC values
are greater than 090 for all types of attacks the proposed algorithm is reversible Bit Error
Rate is calculated between the original and the recovered text As the BER is less than
0006 the proposed algorithm is robust against various attacks
52 FUTURE WORK
This project can be extended for colour images Embedding performance in spatial
domain can be extended to frequency domain Multiple keys are required for the entire
process and their transfer between sender and receiver requires a secure key exchange
protocol These will be the focus on the future work
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
36
REFERENCES
1 Abbasy MR Nikfard P Ordi A Torkaman MRN (2012) bdquoDNA Base Data
Hiding Algorithm‟ International Journal on New Computer Architectures and
Their Applications (IJNCAA) Vol21 pp 183-192
2 Adleman LM (1994) bdquoMolecular computation of solutions to combinatorial
problem‟ Science Vol266 pp 1021-1024
3 Arita M Ohashi Y (2004)‟Secret signatures inside genomic DNA‟
Biotechnology Progress Vol20 pp1605-1607
4 Arya MS Jain N Sisodia J Sehgal N ( 2011) bdquoDNA Encoding Based Feature
Extraction for Biometric Watermarking‟ International Conference on Image
Information Processing (ICIIP 2011)
5 Bandyopadhyay SK Chakraborty S (2011)‟ IMAGE STEGANOGRAPHY
USING DNA SEQUENCE‟ Asian Journal Of Computer Science And
information Technology Vol12 pp 50-52
6 Chakraborty S Bandyopadhyay SK (2012) bdquoTwo Stages Data-Image
Steganography Using DNA Sequence‟ International Journal of Engineering
Research and Development Vol217 pp 69-72
7 Chakraborty S Roy S Bandyopadhyay SK (2012) bdquoImage Steganography
Using DNA Sequence and Sudoku Solution Matrix‟ International journal of
Advanced Research in Computer Science and Software EngineeringVol 22
8 Chang C Lu T Chang Y Lee C(2007) bdquoReversible Data Hiding Schemes for
Deoxyribonucleic Acid Medium‟International Journal of Innovative
Computing Information and Control Vol35 pp1-16
9 Clelland C Risca V Bancroft C (1999) bdquoHiding messages in DNA microdots‟
Nature Vol399 pp 533-534
10 Das P Kar N( 2014) bdquoA DNA Based Image Steganography using 2D Chaotic
Map‟ proceedings of International Conference on Electronics and
Communication Systems (ICECS-2014) pp 149-153
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
37
11 Das P Kar N (2014) bdquo A Highly Secure DNA Based Image Steganography‟
IEEE International Conference On Green Computing Communication And
Electrical Engineering (ICGCCEE‟14)
12 Khalifa A Atito A (2012) bdquoHigh-Capacity DNA-based Steganography‟ The
8th International Conference on INFOrmatics and Systems (INFOS2012) Bio-
inspired Optimization Algonthms and Their Applications Track
13 LJani Anbarasi GSAnandha MalaModigari Narendra ( 2014) bdquoDNA based
Multi-Secret Image Sharing‟ International Conference on Information and
Communication Technologies
14 Leier A Richter C Banzhaf C Rauhe H (2000) bdquo Cryptography with DNA
binary strands‟ BioSystems Vol57 pp 13-22
15 Mousa H Moustafa K Abdel-Wahed W Hadhoud M (2011) bdquoData Hiding
Based on Contrast Mapping Using DNA Medium‟ The International Arab
Journal of Information Technology Vol82 pp147-154
16 Prasenjit DasSubhrajyothi DebNirmalya KarBaby Bhattacharya (2014) bdquoAn
improved DNA based dual cover steganography‟proceeding of international
conference on information and communication technologies
17 Shimanovsky B Feng J Potkonjak M (2002) bdquo Hiding Data in DNA‟ Procs of
the 5th International Workshop in Information Hiding LNCS Vol 2578pp
373-386
18 Shiu H Ng K Fnag JF Lee R Huang C(2010) bdquoData hiding methods based
upon DNA sequences‟ Information of Sciences Vol11 pp 2196-2208
19 Torkaman MRN Nikfard P Kazazi NS Abbasy MR Tabatabaiee SF
(2011)‟Improving Hybrid Cryptosystems with DNA Steganography‟pp 42-
52
20 Weiming Zhang KedeMa NenghaiYu (2013) bdquoReversibility improved data
hiding in encrypted images‟ School of Information Science and Technology
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering
38
LIST OF PUBLICATIONS
1Dhasharathi R Amsaveni A Arunnkumaran GP (2016)ldquoReversible Data Hiding using
Chaotic and 2D Logistic Encryptionrdquo International Conference on Communication and
Security (ICCS-2016) in Pondicherry Engineering College
2 Dhasharathi R Amsaveni A (2016) ldquoChaotic and 2D Logistic Encryption based
Reversible Data Hiding rdquo IEEE Sponsored 3rd
International Conference on Innovation in
Information Embedded and Communication Systems in Karpagam College of
Engineering