Efficient and Secure Biometric Image Stegnography using ...ijcscn.com/Documents/Volumes/vol1issue1/ijcscn2011010114.pdf · Efficient and Secure Biometric Image Stegnography using

Efficient and Secure Biometric Image Stegnography using Discrete Wavelet Transform Sunita Barve, Uma Nagaraj and Rohit Gulabani

Department of Computer Engineering

Maharashtra Academy of Engineering, Alandi

Abstract Steganography is the science of concealing the existence of data in another

transmission medium. It does not replace cryptography but rather boosts the

security using its obscurity features. As proposed method is Biometric

Steganography, here the Biometric feature used to implement Steganography is Skin

tone region of images. Proposed method introduces a new method of embedding

secret data within the skin portion of the image of a person, as it is not that much

sensitive to HVS (Human Visual System). Instead of embedding secret data

anywhere in image, it will be embedded in only skin tone region. This skin region

provides excellent secure location for data hiding. So, firstly skin detection is

performed in cover images and then Secret data embedding will be performed in

DWT domain as DWT gives better performance than DCT while compression. This

biometric method of Steganography enhances robustness than existing methods.

I. INTRODUCTION

Steganography is defined as science or art of hiding (embedding) data in

transmission medium. Steganography is a type of hidden communication that

literally means “covered writing” (from the Greek words stegano or “covered” and

graphos or “to write”). The first use of the term steganography was recorded in

1499 by Johannes Trithemius in his “Steganographia”, a dissertation on

cryptography and steganography disguised as a book on magic. The goal of

steganography is to hide an information message inside harmless cover medium in

such a way that it is not possible even to detect that there is a secret message.

Oftentimes throughout history, encrypted messages have been intercepted but have

not been decoded. While this protects the information hidden in the cipher, the

interception of the message can be just as damaging because it tells an opponent or

enemy that someone is communicating with someone else. Steganography takes the

opposite approach and attempts to hide all evidence that communication is taking

place.

II. LITERATURE SURVEY

The earliest recordings of Steganography were by the Greek historian Herodotus

in his chronicles known as "Histories" and date back to around 440 BC. In the 15th

and 16th century, Romans used invisible inks, which were based on natural

substances such as fruit juices and milk. During the times of WWI and WWII,

significant advances in Steganography took place. Concepts such as null ciphers

(taking the 3rd letter from each word in a harmless message to create a hidden

message, etc), image substitution and microdot (taking data such as pictures and

reducing it to the size of a large period Piece of paper) were introduced and

embraced as great Steganographic techniques.

With the boost of computer power, the internet and with the development

of Digital Signal Processing (DSP), Information Theory and Coding Theory,

Steganography went “Digital”. In the realm of this digital world Steganography has

created an atmosphere of corporate vigilance that has spawned various interesting

applications of the science. Contemporary information hiding was first discussed in

the article “The prisoners’ Problem and the Subliminal Channel” [The prisoner’s

problem]. More recently Kurak and McHugh carried out work which resembled

embedding into the 4LSBs (Least Significant Bits). They discussed image

downgrading and contamination which is now known as Steganography. Cyber-

terrorism, as coined recently, is believed to benefit from this digital revolution.

Figure 1: The prisoner’s problem

Figure 1 elaborates the idea of steganography messages by depicting a scenario

where two prison inmates Bob and Alice try to communicate via the warden

Wendy. Inspired by the notion that Steganography can be embedded as part of the

normal printing process, Japanese firm Fujitsu is pushing technology to encode data

into a printed picture that is invisible to the human eye (i.e., data) but can be

decoded by a mobile phone with a camera.

III. IMAGE STEGANOGRAPHY

Given the proliferation of digital images, especially on the Internet, and given

the large amount of redundant bits present in the digital representation of an image,

images are the most popular cover objects for steganography. In the domain of

digital images, many different image file formats exist, most of them for specific

applications. For these different image file formats, different steganographic

algorithms exist.

A. Image and Transform Domain

Image steganography techniques can be divided into two groups: those in the

Image Domain and those in the Transform Domain. Image – also known as spatial –

domain techniques embed messages in the intensity of the pixels directly, while for

transform – also known as frequency – domain, images are first transformed and

then the message is embedded in the image. Image domain techniques encompass

bit-wise methods that apply bit insertion and noise manipulation and are sometimes

characterized as “simple systems”. The image formats that are most suitable for

image domain steganography are lossless and the techniques are typically dependent

on the image format. Steganography in the transform domain involves the

manipulation of algorithms and image transforms. These methods hide messages in

more significant areas of the cover image, making it more robust. Many transform

Sunita Barve et al, International Journal of Computer Science & Communication Networks,Vol 1(1),September-October 2011

Available online @ www.ijcscn.com 96

ISSN:2249-5789

http://en.wikipedia.org/wiki/Johannes_Trithemius#Steganographia�

domain methods are independent of the image format and the embedded message

may survive conversion between lossy and lossless compression. In the next

sections steganographic algorithms will be explained in categories according to

image file formats and the domain in which they are performed.

IV. SKIN DETECTION

Skin color has proven to be a useful and robust cue for face detection,

localization and tracking. Image content filtering, content aware video compression

and image color balancing applications can also benefit from automatic detection of

skin in images. Face detection and tracking has been the topic of an extensive

research for several decades. Many heuristic and pattern recognition based

strategies have been proposed for achieving robust and accurate solution. Among

feature-based face detection methods, the ones using skin color as a detection cue

have gained strong popularity. Color allows fast processing and is highly robust to

geometric variations of the face pattern. Also, the experience suggests that human

skin has a characteristic color, which is easily recognized by humans. When

building a system, that uses skin color as a feature for face detection, the researcher

usually faces three main problems. First, what color space to choose, second, how

exactly the skin color distribution should be modeled, and finally, what will be the

way of processing of color segmentation results for face detection.

V. COLOUR SPACES Colorimetry, computer graphics and video signal transmission standards have

given birth to many color spaces with different properties. A wide variety of them

have been applied to the problem of skin color modeling.

A. RGB RGB is a color space originated from CRT (or similar) display applications,

when it was convenient to describe color as a combination of three colored rays

(red, green and blue). It is one of the most widely used color spaces for processing

and storing of digital image data. However, high correlation between channels,

significant perceptual non-uniformity, mixing of chrominance and luminance data

makes RGB not a very favorable choice for color analysis and color based

recognition algorithms.

B. YCbCr YCbCr is an encoded nonlinear RGB signal, commonly used by European

television studios and for image compression work. Color is represented by luma

(which is luminance, computed from nonlinear RGB), constructed as a weighted

sum of the RGB values, and two color difference values Cr(Chrominance red) and

Cb(Chrominance blue) that are formed by subtracting luma from RGB red & blue

components. The transformation simplicity and explicit separation of Luminance

and chrominance components make this color space attractive for skin color

modeling.

Figure 2:- RGB to YCbCr Transformation

C. HSV (Hue, Saturation, Value) Hue-saturation based colorspaces were introduced when there was a need for the

user to specify color properties numerically. They describe color with intuitive

values, based on the artist’s idea of tint, saturation and tone. Hue defines the

dominant color (such as red, green, purple and yellow) of an area; saturation

measures the colorfulness of an area in proportion to its brightness. The “intensity”,

“lightness” or “value” is related to the color luminance. The intuitiveness of the

color space components and explicit discrimination between luminance and

chrominance properties made these colorspaces popular in the works on skin color

segmentation. However, several undesirable features of these colorspaces can be

pointed out.

Figure 3:- RGB to YCbCr Transformation

D. Skin Modelling:- The final goal of skin color detection is to build a decision rule that will

discriminate between skin and non-skin pixels. This is usually accomplished by

introducing a metric, which measures distance (in general sense) of the pixel color

to skin tone. The type of this metric is defined by the skin color modeling method.

One method to build a skin classifier is to define explicitly (through a number of

rules) the boundaries skin cluster in some color space. For example:-

The simplicity of this method has attracted (and still does) many researchers.

The obvious advantage of this method is simplicity of skin detection rules that leads

to construction of a very rapid classifier. The main difficulty achieving high

recognition rates with this method is the need to find both good color space and

adequate decision rules empirically. Recently, there have been proposed a method

that uses machine learning algorithms to find both suitable color space and a simple

decision rule that achieve high recognition rates. The authors start with a

normalized RGB space and then apply a constructive induction algorithm to create a

number of new sets of three attributes being a superposition of r, g, b and a constant

1/3, constructed by basic arithmetic operations. A decision rule, which achieves the

best possible recognition, is estimated for each set of attributes. The authors prohibit

construction of too complex rules, which helps avoiding data over-fitting that is

possible in case of lack of training set representativeness. They have achieved

results that outperform Bayes skin probability map classifier in RGB space for their

dataset.

E. Masking and filtering:- Masking and filtering techniques usually restricted to 24 bits or grayscale

images for hiding a message. These methods are similar to paper watermarks,

creating markings in an image. This is achieved for example by modifying the

luminance of parts of the image. While masking changes the visible properties of an

image, it can be done in such a way that the human eye will not notice the

anomalies. Generally masking uses visible aspects of the image; also it is more

robust than LSB modification with respect to compression, cropping and different

kinds of image processing. Although the information is not hidden at the ”noise”

level , rather than it is inside the visible part of the image, which makes it more

suitable than LSB modifications in case a lossy compression algorithm like JPEG is

being used. In skin detection algorithms masking basically means covering the non

skin region with a black mask. Filtering is replacing the white region (representing

the skin portion in the binary image) with the original skin portion in the cover

image. The masking and filtering operation is shown in Fig 2 below:-

Figure 4:- Masking & Filtering Operation



ISSN:2249-5789

VI. 2D HAAR DWT The frequency domain transform we applied in this research is Haar-DWT, the

simplest DWT. A 2-dimensional Haar- DWT consists of two operations: One is the

horizontal operation and the other is the vertical one. Detailed procedures of a 2-D

Haar-DWT are described as follows:

Step 1: At first, scan the pixels from left to right in horizontal direction. Then,

perform the addition and subtraction operations on neighboring pixels. Store the

sum on the left and the difference on the right as illustrated in Figure 4. Repeat this

operation until all the rows are processed. The pixel sums represent the low

frequency part (denoted as symbol L) while the pixel differences represent the high

frequency part of the original image (denoted as symbol H).

Figure 5:- Horizontal Operation on the first row

Step 2: Secondly, scan the pixels from top to bottom in vertical direction. Perform

the addition and subtraction operations on neighboring pixels and then store the sum

on the top and the difference on the bottom as illustrated in Figure 5. Repeat this

operation until all the columns are processed. Finally we will obtain 4 sub-bands

denoted as LL, HL, LH, and HH respectively. The LL sub-band is the low

frequency portion and hence looks very similar to the original image.

Figure 6:- Vertical Operation

The whole procedure which has been described above is called the first-order 2-D

Haar-DWT. The first-order 2-D Haar- DWT applied on the image “Lena” is

illustrated in Figure 6.

Figure 7:- (a) Original image-Lena, (b) Result after the first-order 2-D Haar-

DWT

VII. PROPOSED METHOD FOR DIGITAL IMAGE

STEGNOGRAPHY We are proposing a Digital Image Steganography technique which explores the

Biometric feature of Skin tone region in images. This system should be capable of

embedding secret images using the Discrete Wavelet Transform. The primary goal

driving us is building a robust and secure Steganography system with additional

security features as compared to the existing systems. Proposed method creating

high quality stego image their by increasing security.

The detailed algorithm for the encoder is discussed below:-

1. Select the message to be embedded, preprocess it.

2. Select the cover image, with optimum number of skin pixels

3. Segment out the skin pixels.

4. Select the particular region of skin, where to embed the message.

(Hereafter referred as Skin ROI)

5. Take 2D wavelet transform of the skin ROI.

Figure 8:- Proposed Encoder

6. Select the particular sub band to be used for embedding.

7. Perform the payload check.

8. Embed the preprocessed data in particular sub band.

9. Perform Inverse DWT

10. We obtain the stego image.

Fig 9. Region of Interest

The decoder end has been purposefully not been kept exactly inverse of the

encoder. Keeping in mind the general tendency of assuming the decoder to be the

inverse of encoder, we have implemented the IDWT on the encoder end and no

inverse transform on the decoder end. This acts as a very important security feature

and increases the robustness of our algorithm.

The following are the steps to be implemented while decoding the stego-image.

1. Apply key 1 to the stego image

2. Perform DWT on the selected sub band

3. Carry out LSB extraction to get a distorted image

4. Reduce noise components in the image

5. Result is the hidden image

VIII. ADDITIONAL FEATURE Whenever security enhancements have been proposed till date, the first

question that arises in the mind of a third person is to try breaking it. History is

witness to this fact, and hence as an outcome we have ever advancing fields of



ISSN:2249-5789

Cryptography, Steganography and so forth. Our proposed framework already

encompasses three-tier robustness with the existence of two keys and IDWT on the

encoder end. We know that there is no bound to the thoughts of a negative mind.

Yet considering a scenario in which a Hacker manages to obtain the stego image

along with the two keys. In such a case too he shall be unsuccessful in decoding the

message. As mentioned earlier, our proposed decoder is not the exact inverse of the

encoder. Thus if the Hacker tries to reverse the encoder upon the stego image and if

he tries to fiddle with the bits, all he obtains is a completely distorted image. This

challenge is a proof to the robustness in steganography that we’re proposing.

6.

Figure 10:- Proposed Decoder

IX. ANALYSIS To establish an objective criterion for digital image quality, a parameter named

PSNR (Peak Signal to Noise Ratio) is defined as follows:

MSE (Mean Square Error) stands for the mean-squared difference between the

cover-image and the stego-image. The mathematical definition for MSE is:

Where aij means the pixel value at position (i, j) in the cover image and bij

means the pixel value at the same position in the corresponding stego-image. The

calculated PSNR usually adopts dB value for quality judgment. The larger PSNR is,

the higher the image quality is (which means there is only little difference between

the cover-image and the stego-image). On the contrary, a small dB value of PSNR

means there is great distortion between the cover-image and the stego-image. For

color images, the reconstruction of all three color spaces must be considered in the

PSNR calculation. The MSE is calculated for the reconstruction of each color space.

The average of these three MSEs is used to generate the PSNR of the reconstructed

RGB image (as compared to the original 24-bit RGB image). The color PSNR

equations are as follows:-

MSE red (or green or blue) is similar to the main MSE equation for each color

space.

Fig 11. Comparison of Cover and Stego Image

X. CONCLUSION Digital Steganography is a fascinating scientific area which falls under the

umbrella of security systems. Proposed framework is based on steganography that

uses Biometric feature i.e. skin tone region. Skin tone detection plays a very

important role in Biometrics and can be considered as secure location for data

hiding. Secret data embedding is performed in DWT domain than the DCT as DWT

outperforms than DCT. Using Biometrics resulting stego image is more tolerant to

attacks and more robust than existing methods.

REFERENCES 1. Abbas Cheddad, Joan Condell, Kevin Curran and Paul Mc Kevitt,

“Biometric Inspired Digital Image Steganography”, 15th Annual IEEE International Conference and Workshop on the Engineering of Computer Based Systems, 978-0-7695-3141-0/08 $25.00 © 2008 IEEE DOI 10.1109/ECBS.2008.11.159)

2. Abbas Cheddad, Joan Condell, Kevin Curran and Paul Mc Kevitt, “A Skin Tone Detection Algorithm for an Adaptive Approach to Steganography”, Faculty of Computing and Engineering, University of Ulster, BT48 7JL, Londonderry, Northern Ireland, United Kingdom.

3. Po- Yueh Chen and Hung-Ju Lin, “A DWT Based Approach for Image Steganography”, Department of Computer Science and Information Engineering, National Changhua University of Education, No. 2 Shi-Da Road, Changhua City 500, Taiwan, R.O.C.

4. Vladimir Vezhnevets and Vassili Sazonov, “A Survey on Pixel-Based Skin Color Detection Techniques”, Alla Andreeva Graphics and Media Laboratory, Faculty of Computational Mathematics and Cybernetics Moscow State University, Moscow, Russia.

5. Neil F. Johnson and Sushil Jajodia, “Steganalysis: The Investigation of Hidden Information,” IEEE conference on Information Technology, pp. 113-116, 1998.

6. Lisa M.Marvel and Charles T. Retter, “A Methodlogy for Data Hiding using Images,” IEEE conference on Military communication, vol. 3, Issue. 18-21, pp. 1044-1047, 1998.

7. Giuseppe Mastronardi, Marcello Castellano, Francescomaria Marino, “Steganography Effects in Various Formats of Images. A Preliminary Study,” International Workshop on Intelligent data Acquisition and Advanced Computing Systems: Technology and Applications, pp. 116-119, 2001.

8. LIU Tong, QIU Zheng-ding “A DWT-based color Images steganography Scheme” IEEE International Conference on Signal Processing, vol. 2, pp.1568-1571, 2002.

9. Jessica Fridrich, Miroslav Goijan and David Soukal, “Higher-order statistical steganalysis of palette images” Proceeding of SPIE, Electronic Imaging, Security, Steganography, and Watermarking of

a. Multimedia ContentsV, vol. 5020, pp. 178-190, 2003. 10. Jessica Fridrich and David Soukal, “Matrix Embedding for Large

Payloads” SPIE, Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents , vol. 6072, pp. 727-738. 2006.

11. Yuan-Yu Tsai, Chung-Ming Wang “A novel data hiding scheme for color images using a BSP tree” Journal of systems and software, vol.80, pp. 429-437, 2007.

12. Jun Zhang, Ingemar J. Cox and Gwenael Doerr.G “Steganalysis for LSB Matching in Images With High-frequency Noise” IEEE Workshop on Multimedia Signal Processing, issue 1-3, pp.385- 388, 2007.

13. M. Mahdavi, Sh. Samavi, N. Zaker and M. Modarres-Hashemi, “Steganalysis Method for LSB Replacement Based on Local Gradient of Image Histogram,” Journal of Electrical and Electronic Engineering, vol. 4, no. 3, pp. 59-70, 2008.



ISSN:2249-5789