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ISSN 2319-7080 International Journal of Computer Science and Communication Engineering Volume 3 issue 1(February 2014 issue)
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LITERATURE SURVEY: Steganography Using Redundant
Bit Replacement By Neural Network Jasmeet Kaur
1, Nitika Kapoor
2, Harish Kundra
3
1Research Scholar,
2,3Assistant Professor
1,2,3 Department of Computer Science and Engineering, Rayat Institute of Engineering and Information Technology
Railmajra, SBS Nagar, (Punjab) INDIA [email protected], [email protected], [email protected]
Abstract: - The paper describes the progress in the field of
Steganography. The idea behind this technique is to hide
the information in the media. The challenge is to make the
hidden information untraceable. The concept originate from
spatial domain to more enhanced technique. The proposed
technique in this paper is Neural Network. By using this
technique we make hide the information in better way than
simpler techniques in spatial domain.
I. INTRODUCTION
Classic methods of securing communication mainly base on
cryptography, which encrypts plain text to generate cipher text.
However, the transmission of cipher text may easily arouse
attackers‟ suspicion, and the cipher text may thus be
intercepted, attacked or decrypted violently. In order to make up
for the shortcomings of cryptographic techniques, steganography
has been developed as a new covert communication means in
recent years. It transfers message secretly by embedding it into a
cover medium with the use of information hiding techniques.
Cryptography and Steganography are two important branches of
information security. Cryptography provides encryption
techniques for a secure communication. Cryptography is the
science that studies the mathematical techniques for keeping
message secure and free from attacks [6]. Steganography is the
art and science of hiding communication. The word
steganography is derived from the Greek word stegos‖ meaning
cover‖ and grafia‖ meaning writing‖ defining it as ―covered
writing .Steganography involves hiding information so it appears
that no information is hidden at all. Steganalysis is the science of
detecting hidden information. The goal of steganalysis is to
break steganography. Steganalysis deals with three important
attacks. (a) Visual attacks: one can identify the stego image with
the naked eyes (b) Statistical attacks: they reveal the smallest
alterations in an image statistical behaviour. It is further
subdivided into (i) Passive attack: identifying the presence or
absence of a covert messages or embedding algorithm used (ii)
Active attacks: used to investigate embedded message length or
hidden message location or secret key used in hidden process (c)
Structural attacks: identifying the changes in the cover file.
Steganography is employed in various useful applications, e.g.,
copyright control of materials, enhancing robustness of image
search engines and smart IDs (identity cards) where individuals‟
details are embedded in their photographs. Other applications are
video-audio synchronization, companies‟ safe circulation of
secret data, TV broadcasting, TCP/IP packets (for instance a
unique ID can be embedded into an image to analyze the
network traffic of particular users), and also checksum
embedding [8]. One method of common Steganography
technique is to hide the secret message in the least significant
bits of pixels of the cover image. The image quality of stego
image achieved by applying the LSB technique is very closer to
the original one. But the drawback is it cannot survive image
processing manipulations. One method of LSB Steganography
involves manipulating the LSB plane from direct replacement of
the cover image with message bits to some type of logical or
arithmetic combination between two. Several examples of LSB
techniques are found. This technique achieves both high
capacity and low perceptibility. But it is not very sophisticated
and subject to extraction by unwanted persons. Masking and
filtering techniques usually restricted to 24 bits or grayscale
images. These methods are effectively similar to „paper
watermarks‟, creating markings in an image. This can be
achieved for example by modifying the luminance of parts of the
image. While masking does change the visible properties of an
image, it can be done in such a way that the human eye will not
notice the anomalies. Least Significant Bit maintains a good
visual quality of stego-image, it can hide little information.
Considering the drawback of LSB, some methods begin to
take account of the visual identity that human eyes are
insensitive to edged and textured areas when embedding secret
information, such as BPCS(biplane complexity
segmentation),PVD(pixel value differencing), MBNS (multiple
base notational system ), SOC, Side Match and WCL. The
capacity of embedded information is thereby greatly improved
while the quality of visual imperceptibility is maintained. As
human vision sensitivity is complex, it is hard to exactly
decide whether a pixel is in less sensitivity areas or not.
Thus, based on the contrast and texture sensitivity, we train
self-organizing map Neural Networks (NNs) trained to
distinguish pixels in less sensitive areas from pixels in more
sensitive areas. So, NNs trained is the secret key. Then,
we use NNs trained to classify pixels, and select pixels
in less sensitive areas to embed more secret data. On the
receiving side, the original image is not needed for
extracting the embedded data. Neural approach adds the
complexity for the hackers accessing and also presents high
potentiality in defense operations. Neural Steganography is a
powerful tool that enables people to communicate without
possible eavesdroppers even knowing there is a form of
communication. Basic elements of steganography in images are
shown in Figure 1. The carrier image in steganography is
ISSN 2319-7080 International Journal of Computer Science and Communication Engineering Volume 3 issue 1(February 2014 issue)
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called the "cover image" and the image which has the
embedded data is called the "stego image". The embedding
process is usually controlled using a secret key shared
between the communicating parties.
Fig:1 Typical Element Of Steganography System.
II. RELATED WORK
Adel Almohammad , Gheorghita Ghinea and Robert M. Hierons
in 2009 proposed a High Capacity Steganographic Method
Based Upon the JPEG standard which uses 8x8 quantization
tables, but it does not specify any default or standard values for
quantization tables. However, the JPEG standard provides a pair
of quantization tables as examples tested empirically and found
to generate good results. Dividing this quantization table they
get a new quantization table. Using this new quantization table
generates reconstructed images almost identical to the source
image. Therefore, this table will be used with Jpeg-Jsteg method
in the experiment. Since the values of these tables could be an
arbitrary choice, some researchers modified these quantization
tables for their research purposes. A quantization table can
arbitrarily be generated. Consequently, They produced a 16x16
quantization table by simulating and stretching the scaled
quantization table [3]. Kousik Dasgupta1, J.K. Mandal2 and
Paramartha Dutta in April 2012 suggested a Hash Based LSB
Technique for Video Steganography that deals with hiding secret
data or information within a video. A spatial domain technique
where the secret information is embedded in the LSB of the
cover frames. Eight bits of the secret information is divided into
3,3,2 and embedded into the RGB pixel values of the cover
frames respectively. A hash function is used to select the
position of insertion in LSB bits. The proposed method is
analyzed in terms of both Peak Signal to Noise Ratio (PSNR)
compared to the original cover video as well as the Mean Square
Error (MSE) measured between the original and steganography
files averaged over all video frames. The proposed technique is
compared with existing LSB based steganography and the
results are found to be encouraging [1]. Some of few researchers
have already implemented NEURAL NETWORK in their
approach for the same here are some reviews about them. Usha
B A1, Dr. N K Srinath2, Dr. N K Cauvery in May 2013
proposed a Data Embedding Technique using Neural Network.
According to them the neural approach to embed information
satisfies a secure steganography. Neural approach adds the
complexity for the hackers accessing and also presents high
potentiality in defense operations. Neural Steganography is a
powerful tool that enables people to communicate without
possible eavesdroppers even knowing there is a form of
communication [5]. Imran Khan in August 2013 suggested an
Efficient Neural Based Algorithm of Steganography for Image.
To provide large capacity of the hidden secret data and to
maintain a good visual quality of stego-image a novel
steganography method based on neural network and random
selection of edged areas of pixels is proposed . Firstly a cover
image is divided into a non-overlapping two pixels block and
this pixel block generates a set of edged non-overlapping
regions. After this a neural network is applied which generates a
stego-image which has been immune against conventional attack
and performs good perceptibility compared to other
steganography approaches. From our experimental results it can
be shown that the proposed method hides information in edged
regions and maintains a better visual display of steganography
image than the traditional methods [8]. Bhavneet Kaur, Pooja &
Harish Kundra in December2013 proposed a Performance
Enhancement of a Transform Domain based Steganograhic
Technique using Segmentation. This Method involves
combining the DCT algorithm along with NEURAL
NETWORK in such a way that the IMAGE QUALITY which is
measured in terms of PSNR increases and the data remains safe
within the image [9].
III. TAXONOMYOF STEGANOGRAPHIC
TECHNIQUES
There are quite a lot of approaches in classifying steganographic
techniques. These approaches can be classified in accordance
with the type of covers used with secret communications [10].
Steganographic techniques that modify image files for hiding
information include the following:
• Spatial domain
•Transform domain
• Distortion techniques
File Embedding technique
1. SPATIAL DOMAIN TECHNIQUE
Spatial domain steganography techniques, also known as
substitution techniques, are a group of relatively simple
techniques that create a covert channel in the parts of the cover
image in which changes are likely to be a bit scant when
compared to the human visual system (HVS).One of the ways to
do so is to hide information in the least significant bit (LSB) of
the image data.
1.1LSB TECHNIQUE: This embedding method is basically
based on the fact that the least significant bits in an image can be
thought of as random noise, and consequently they become not
responsive to any changes on the image. The disadvantage of
this technique is that it uses each pixel in the image. As a result,
ISSN 2319-7080 International Journal of Computer Science and Communication Engineering Volume 3 issue 1(February 2014 issue)
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if lossy compression is used, some of the hidden information
might be lost [10].
Limitation of LSB
LSB technique in the spatial domain is a practical way to
conceal information but, at the same time, it is vulnerable to
small changes resulting from image processing or lossy
compression [7]. Although LSB techniques can hide large
quantities of information i.e., high payload capacity, they often
compensate the statistical properties of the image and thus
indicate a low robustness against statistical attacks as well as
image manipulation.
2. TRANSFORM DOMAIN TECHNIQUE
Transform domain embedding can be defined as a domain of
embedding techniques for which a number of algorithms have
been suggested. The process of embedding data in the frequency
domain of a signal is much stronger than embedding principles
that operate in the time domain. It is worth saying that most of
the strong steganographic systems today operate within the
transform domain. Transform domain techniques have an
advantage over LSB techniques because they hide information in
areas of the image that are less exposed to compression,
cropping, and image processing. Some transform domain
techniques do not seem dependent on the image format and they
may outrun lossless and lossy format conversions.
2.1 JPEG COMPRESSION: If an image is to compress into
JPEG format, the RGB color space is first turned into a YUV
representation. Through this representation, the Y component
represents brightness (or luminance) and the U and V
components stand for color (or chrominance). It is known that
the human eye is more sensitive to changes in the brightness of a
pixel than to changes in its color. Down sampling the color
information is taken as an advantage of the JPEG to reduce the
size of the file where the color components (U and V) are
splitted in the horizontal and vertical directions and
consequently reducing the file size by a factor of 2. Then, the
image is transformed. For JPEG images, the discrete cosine
transform (DCT) is used; the pixels can be converted with such
mathematical processing by simply “spreading” the position of
the pixel values over the image or part of it [12]. With DCT
transformation, a signal is transformed from the representation
of an image into the frequency domain, this is done by sorting
the pixels into (8 × 8) pixel blocks and transforming these blocks
into 64-DCT coefficients which are affected by any modification
of a single DCT coefficient.
2.2 Wavelet transform technique: Wavelets transform (WT)
converts spatial domain information to the frequency domain
information. Wavelets are used in the image steganographic
model because the wavelet transform clearly partitions the high-
frequency and low-frequency information on a pixel by pixel
basis. The discrete wavelet transform (DWT) method is favored
over the discrete cosine transform (DCT) method, owing to the
resolution that the WT provides to the image at various levels
.Wavelets are mathematical functions that divide data into
frequency components, which makes them ideal for image
compression. In contrast with the JPEG format, they are far
better at approximating data with sharp discontinuities .
Researchers use vector quantization, called Linde-Buzo-Gray
(LBG), associated with block codes, known as BCH codes, and
one-stage discrete Haar wavelet transforms. They emphasize that
modifying data by using a wavelet transformation produces good
quality with few perceptual artifacts. A group of scientists at
Iowa State University are developing an advanced application
called artificial neural network technology for steganography
(ANNTS), with the aim of detecting all current steganography
methods, which include DCT, DWT, and DFT. They found that
the inverse discrete Fourier transform (IDFT) includes a
rounding error that makes DFT inappropriate for steganography
applications [12]. The promising techniques such as DCT, DWT
and the adaptive steganography are not tended to attacks,
especially when the hidden message is small. This can be
justified in relation to the way they change the coefficients in the
transform domain, thus, image distortion is kept to a minimum.
Generally speaking, such techniques tend to have a lower
payload when they are compared to the spatial domain
algorithms [8]. The experiments on the discrete cosine transform
(DCT) coefficients have introduced some promising results and
then they have diverted the researchers‟ attention towards JPEG
images. Working at some level like that of DCT turns
steganography much more powerful and less prone to statistical
attacks. Embedding in the DWT domain reveals a sort of
constructive results and outperforms DCT embedding, especially
in terms of compression survival
3. DISTORTION TECHNIQUES
Distortion techniques require knowledge of the original cover
image during the decoding process where the decoder functions
to check for differences between the original cover image and
the distorted cover image in order to restore the secret message.
The encoder, on the other hand, adds a sequence of changes to
the cover image. So, information is described as being stored by
signal distortion. Using this technique, a stego-object is created
by applying a sequence of modifications to the cover image.
This sequence of modifications is selected to match the secret
message required to transmit. The message is encoded at
pseudo-randomly chosen pixels. If the stego-image is different
from the cover image at the given message pixel, then the
message bit is a “1.”[10] Otherwise, the message bit is a “0.”
The encoder can modify the “1” value pixels in such manner that
the statistical properties of the image are not affected (which is
different from many LSB methods). However, the need for
sending the cover image limits the benefits of this technique.
4. FILE EMBEDDING TECHNIQUE
Different image file formats are known for having different
header file structures. In addition to the data values, such as
pixels, palette, and DCT coefficients, secret information can also
be hidden in either a header structure or at the end of the file
[12]. For example, the comment fields in the header of JPEG
images usually contain data hidden by the invisible Secrets and
ISSN 2319-7080 International Journal of Computer Science and Communication Engineering Volume 3 issue 1(February 2014 issue)
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Steganozorus. Camouflage, JpegX, PGE10, and PGE20 add data
to the end of a JPEG image.
Limitation Of File Embedding Technique
File formatting techniques can store large amounts of
information, but they are easily detected and attacked.
LSB TRANSFORM
DOMAIN
FILE EMBEDDING DISTORTION
IMPERCEPTIBILITY High High High Low
ROBUSTNESS Low High Low Low
PAYLOAD
CAPACITY
High Low High Low
Table 1: Comparison Of Different Techniques.
IV. TECHNIQUE INVOLVED
NEURAL NETWORK
An Artificial Neural Network (ANN) is an information
processing paradigm that is inspired by the way biological
nervous systems, such as the brain, process information. The key
element of this paradigm is the novel structure of the
information processing system. It is composed of a large number
of highly interconnected processing elements (neurons) working
in unison to solve specific problems. ANNs, like people, learn
by example. An ANN is configured for a specific application,
such as pattern recognition or data classification, through a
learning process. Learning in biological systems involves
adjustments to the synaptic connections that exist between the
neurons. This is true of ANNs as well.
Why use neural networks?
Neural networks, with their remarkable ability to derive meaning
from complicated or imprecise data, can be used to extract
patterns and detect trends [8] that are too complex to be noticed
by either humans or other computer techniques. A trained neural
network can be thought of as an "expert" in the category of
information it has been given to analyze. This expert can then be
used to provide projections given new situations of interest and
answer "what if" questions.
Other advantages include:
1. Adaptive learning: An ability to learn how to do tasks based
on the data given for training or initial experience.
2. Self-Organization: An ANN can create its own organization
or representation of the information it receives during learning
time.
3. Real Time Operation: ANN computations may be carried out
in parallel, and special hardware devices are being designed and
manufactured which take advantage of this capability.
4. Fault Tolerance via Redundant Information Coding: Partial
destruction of a network leads to the corresponding degradation
of performance. However, some network capabilities may be
retained even with major network damage.
A simple neuron
An artificial neuron is a device with many inputs and one output.
The neuron has two modes of operation; the training mode and
the using mode. In the training mode, the neuron can be trained
to fire (or not), for particular input patterns. In the using mode,
when a taught input pattern is detected at the input, its associated
output becomes the current output. If the input pattern does not
belong in the taught list of input patterns, the firing rule is used
to determine whether to fire or not.
aa s
A simple neuron
V. CONCLUSION AND FUTURE WORK
This paper provides an overview of steganography & reviewed
the main steganographic techniques. Each of these techniques
tries to satisfy the three most important factors of steganographic
design (imperceptibility or indefectibility, capacity, and
robustness). We can deduce that while one technique may lack
in payload capacity, another may lack in robustness. For
example, file formatting techniques can store large amounts of
information, but they are easily detected and attacked. Likewise,
LSB techniques in a spatial domain have a high payload
capacity, but they often fail to prevent statistical attacks and are
thus easily detected. Besides, file and spatial domain approaches
are considered not to be robust against lossy compression and
filtering. Transform domain techniques are considered more
robust for lossy compression image formats, but this advantage
is achieved at the expense of payload capacity. However, it is
possible to defeat the transform domain techniques using Neural
Network. Neural network generates a stego-image which has
been immune against conventional attack and performs good
perceptibility compared to other steganographic approaches.
ISSN 2319-7080 International Journal of Computer Science and Communication Engineering Volume 3 issue 1(February 2014 issue)
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