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 15 th and 16 th 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
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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
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.
Sunita Barve et al, International Journal of Computer Science & Communication Networks,Vol 1(1),September-October 2011