Image Steganalysis: Segmenting Stego Image using Watershed Method and Feature Extraction by MRF Method

B. Yamini*, R. Sabitha**
* Research Scholar, Department of Computer Science and Engineering, Sathyabama University, Chennai, Tamil Nadu, India.
** Professor and Head, Department of Information Technology, Jeppiaar Engineering College, Chennai, Tamil Nadu, India.
Periodicity:March - May'2016
DOI : https://doi.org/10.26634/jpr.3.1.8105

Abstract

Transmitting the secret information by Steganography plays a vital role in Human Visual System (HVS). The carrier media such as image or audio or video can be used to hide the information. Steganalysis is a technique used to get rid of cheating, by identifying the hidden information from the carrier media. The identification of embedded information or message from the carrier media produces higher success rate to steganography methods. Image Steganography is the art of hiding the message or a file or an image by taking the image as carrier media. Based on the adaptable regions, the content is hidden and this method is termed as Adaptive image steganography. Dealing with retrieval of embedded content from the adaptable region of cover image is known to be Adaptive Image Steganalysis. The Blind Steganalysis is the ability to attack the stego image without the knowledge about steganography. Its Counter method, attacks the stego image by significant method used for steganography. In existing method, Enhanced canny edge detector is used to extract the features of the image better than other edge detectors, but smoothens the boundaries including noise and fails to identify the false edges. In the proposed method, Watershed method is used to segment the Adaptive regions from the stego image. The Markov Random Fields (MRF) extracts the features from the segmented adaptive region. The precision and recall is calculated after identifying the adaptive region with its payload location and hidden content using SVM (Support Vector Machine) classifier. An SVM is a binary classifier, classifies data by finding the best hyperplane which separates all data points of one class from the other class. After the classification and identification of payload location, the message is extracted from the hidden region by reversible two LSB (Least Significant Bit) bits.

Keywords

Adaptive Steganography, Blind Steganalysis, Watershed Method, Markov Random Fields, Support Vector Machine, Least Significant Bit, Enhanced Canny Operator

How to Cite this Article?

Yamini, B., and Sabitha, R. (2016). Image Steganalysis: Segmenting Stego Image using Watershed Method and Feature Extraction by MRF Method. i-manager’s Journal on Pattern Recognition, 3(1), 23-30. https://doi.org/10.26634/jpr.3.1.8105

References

[1]. Miroslav Goljan, Jessica Fridrich and Rémi Cogranne, (2014). “Rich Model for Steganalysis of Color Images”. IEEE Workshop on Information Forensics and Security (WIFS), pp.185-190.
[2]. S. Geetha, S. Sindhu, R. Renganathan, P. Janaki Raman and N. Kamaraj, (2008). “StegoHunter: Steganalysis of LSB Embedded Images Based on Stego- Sensitive Threshold Close Color Pair Signature”. Sixth Indian Conference on Computer Vision, Graphics & Image Processing ICVGIP '08, pp.281-288.
[3]. Arun R, Nithin Ravi S, and Thiruppathi K, (2012). “Intra block and Inter block Neighboring Joint density based approach for JPEG Steganalysis”. Intl. Journal on Soft Computing, Vol.3, No.2, pp.63-70.
[4]. Lamia Jaafar Belaid and Walid Mourou, (2009). “Image segmentation: A watershed transformation algorithm”. Image Analysis Stereology, Vol.28, pp.93-102.
[5]. E. Punarselvam, P. Suresh, R. Parthasarathy, and M. Suresh, (2013). “Segmentation of Lumbar Spine Image Using Watershed Algorithm”. International Journal of Engineering Research and Applications, ISSN: 2248- 9622, Vol.3, No.6, pp.1386-1389
[6]. Jos B.T.M. Roerdink and Arnold Meijster, (2001). “The Watershed Transform: Definitions, Algorithms and Parallelization Strategies”. Fundamental Informaticae, Vol.41.
[7]. Tara Saikumar, P. Yugander, P. S. Murthy, and B. Smitha, (2013). “Image Segmentation Algorithm Using Watershed Transform and Fuzzy C-Means Clustering on Level Set Method”. International Journal of Computer Theory and Engineering, Vol.5, No.2.
[8]. Ehsan Nadernejad, Sara Sharifzadeh and Hamid Hassanpour, (2008). “Edge Detection Techniques: Evaluations and Comparisons”. Applied Mathematical Sciences, Vol.2, No.31, pp.1507-1520.
[9]. Weiqi Luo, Fangjun Huang, and Jiwu Huang, (2010). “Edge Adaptive Image Steganography Based on LSB Matching Revisited”. IEEE Transactions on Information Forensics and Security, Vol.5, No.2.
[10]. Soodeh Bakhshandeh, Javad Ravan Jamjah, and Bahram Zahir Azami, (2009). “Blind Image Steganalysis Based on Local Information and Human Visual System”. Signal Processing, Image Processing and Pattern Recognition, Communications in Computer and Information Science, Vol.61, pp.201-208.
[11]. D. Y. Chen, S. P. Zhong, (2012). “A Universal Steganalysis Algorithm for JPEG Image Based on Selective SVMs Ensemble”. Advanced Materials Research, Vol.532-533, pp.1548-1552.
12. Fridrich J, Soukal D, and Goljan M. (2005). “Maximum likelihood estimation of length of secret message embedded using ±k steganography in spatial domain”. Proceedings of IS&T/SPIE Electronic Imaging: Security, Steganography, and Watermarking of multimedia Contents, San Jose, California, USA. Bellingham, Wash: SPIE, pp.595-606.
[13]. Fridrich J, Goljan M and Du R, (2001). “Steganalysis based on JPEG compatibility”. Special Session on Theoretical and Practical Issues in Digital Watermarking and Data Hiding, August 20-24, Denver, CO. SPIE Multimedia Systems and Applications, pp.275-280.
[14]. Holotyak T, Fridrich J, and Soukal D, (2005). “Stochastic approachto secret message length estimation in ±k embedding steganography ”. Proceedings of IS&T/SPIE Electronic Imaging: Security, Steganography, and Watermarking of multimedia Contents, San Jose, California, USA. Bellingham, Wash: SPIE, pp.673-684.
[15]. Ker A. D, (2005). “Steganalysis of LSB matching in grayscale images”. Signal Process Letters, IEEE, Vol.12, No.6, pp.441-444.
[16]. Luo Weiqi, Wang Yuangen, and Huang Jiwu, (2011). “Security analysis on spatial ±1 steganography for JPEG decompressed images”. Signal Processing Letters, IEEE, Vol.18, No.1, pp.39-42.
[17]. Tawfiq Abdulkhaleq Abbas, and Hassanein Karim Hamza, (2014). “Steganography Using Fractal Images Technique”. IOSR Journal of Engineering (IOSRJEN), Vol.4, No.2, pp.52-61.
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