Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31198
DWT Based Image Steganalysis

Authors: Indradip Banerjee, Souvik Bhattacharyya, Gautam Sanyal

Abstract:

‘Steganalysis’ is one of the challenging and attractive interests for the researchers with the development of information hiding techniques. It is the procedure to detect the hidden information from the stego created by known steganographic algorithm. In this paper, a novel feature based image steganalysis technique is proposed. Various statistical moments have been used along with some similarity metric. The proposed steganalysis technique has been designed based on transformation in four wavelet domains, which include Haar, Daubechies, Symlets and Biorthogonal. Each domain is being subjected to various classifiers, namely K-nearest-neighbor, K* Classifier, Locally weighted learning, Naive Bayes classifier, Neural networks, Decision trees and Support vector machines. The experiments are performed on a large set of pictures which are available freely in image database. The system also predicts the different message length definitions.

Keywords: Neural Networks, Steganalysis, Decision trees, SVM, naive Bayes classifier, moments, kNN, LWL, wavelet domain

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1096221

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2253

References:


[1] Abbas Cheddad, Joan Condell, Kevin Curran, Paul Mc Kevitt, Digital image steganography: Survey and analysis of current methods Signal Processing 90, 2010,pp. 727– 752.
[2] Indradip Banerjee, Souvik Bhattacharyya, Gautam Sanyal, "Robust image steganography with pixel factor mapping (PFM) technique", Computing for Sustainable Global Development (INDIACom), 2014 International Conference on 5-7 March 2014. Page(s): 692 - 698, Print ISBN: 978-93-80544-10-6. Publisher: IEEE Xplore Digital Library.
[3] Jim Bartel. "Steganalysis: An Overview" Security Essentials Bootcamp Style (Security 401). Global Information Assurance Certification Paper.
[4] Avcıbaş, İ., Memon N., Sankur B., "Steganalysis using image quality metrics”, IEEE Trans. on Image Process., January 2003.
[5] Gojan, J., M. Goljan, R. Du, "Reliable detection of LSB steganography in color and grayscale images”, Proc., of the ACM Workshop on Mult. And Secur., Ottawa, CA, pp. 27-30, October 5, 2001.
[6] Johnson, N.F., S. Jajodia, "Steganalysis of images created using current steganography software”, in David Aucsmith (Ed.): Information Hiding, LNCS 1525, pp. 32-47. Springer-Verlag Berlin Heidelberg, 1998.
[7] Westfeld, A. Pfitzmann, "Attacks on steganographic systems”, in Information Hiding, LNCS 1768, pp. 61-66, Springer-Verlag Heidelberg, 1999.
[8] Philip Bateman, Hans Georg Schaathun. "Image Steganography and Steganalysis” Thesis for the Degree of Master of Science in Security Technologies & Applications at Department of Computing, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, Surrey, United Kingdom.4th August 2008.
[9] A. Westfeld, A. P¯tzmann. Attacks on steganographic systems - breaking the steganographic utilities ezstego, jsteg, steganos, and s-tools-and some lessons learned. In Proceedings of the 3rd Information Hiding Workshop, volume 1768 of LNCS, pages 61-76. Springer, 1999.
[10] Niels Provos, Peter Honeyman. Detecting steganographic content on the internet. In Proceedings of NDSS'02: Network and Distributed System Security Symposium, pages 1-13. Internet Society, 2002.
[11] Jessica Fridrich, Miroslav Goljan, Rui Du. Reliable detection of lsb steganography in color and grayscale images. In Proceedings of 2001 ACM workshop on Multimedia and security: new challenges, pages 27-30. ACM Press, 2001.
[12] S. Dumitrescu, X. L. Wu, Z. Wang. Detection of lsb steganography via sample pair analysis. IEEE Transactions on Signal Processing, 51(7):1995-2007, 2003.
[13] J. Fridrich, M. Goljan. On estimation of secret message length in lsb steganography in spatial domain. In IS&T/SPIE Electronic Imaging: Security, Steganography, and Watermarking of Multimedia Contents VI, volume 5306, pages 23-34. SPIE, 2004.
[14] D. Ker. Fourth-order structural steganalysis and analysis of cover assumptions. In IS&T/SPIE Electronic Imaging: Security, Steganography, and Watermarking of Multimedia Contents VIII, volume 6072, pages 1-14. SPIE, 2006.
[15] A. D. Ker. A general framework for the structural steganalysis of lsb replacement. In Proceedings of the 7th Information Hiding Workshop, volume 3727 of LNCS, pages 296-311. Springer, 2005.
[16] J. Harmsen, W. Pearlman. Steganalysis of additive noise modelable information hiding. In IS&T/SPIE Electronic Imaging: Security, Steganography, and Watermarking of Multimedia Contents V, volume 5020, pages 131-142. SPIE, 2003.
[17] Jinggang Huang, David Mumford. Statistics of natural images and models. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, volume 1, pages 541-547, 1999.
[18] J. H. He, J. W. Huang, G. P. Qiu. A new approach to estimating hidden message length in stochastic modulation steganography. In Proceedings of the 4th Internation Workshop on Digital Watermarking, volume 3710 of LNCS, pages 1-14. Springer, 2005.
[19] J. H. He, J. W. Huang. Steganalysis of stochastic modulation steganography. Science in China Series: F-Information Sciences, 49(3):273-285, 2006.
[20] M. Niimi, R. O. Eason, H. Noda, E. Kawaguchi. Intensity histogram steganalysis in bpcs steganography. In IS&T/SPIE Electronic Imaging: Security and Watermarking of Multimedia Contents III, volume 4314, pages 555-564. SPIE, 2001.
[21] X. P. Zhang, S. Z. Wang. Vulnerability of pixel-value di®erencing steganography to histogram analysis and modi¯cation for enhanced security. Pattern Recognition Letters, 25(3):331-339, 2004.
[22] Bin Li, Yanmei Fang, Jiwu Huang. Steganalysis of multiple-base notational system steganography. IEEE Signal Processing Letters, 15:493-496, 2008.
[23] K. Sullivan, Z. Bi, U. Madhow, S. Chandrasekaran, B. S. Manjunath. Steganalysis of quantization index modulation data hiding. In Proceedings of 2004 IEEE International Conference on Image Processing, volume 2, pages 1165-1168, 2004.
[24] J. Fridrich, M. Goljan, D. Hogea. Steganalysis of jpeg images: Breaking the f5 algorithm. In Proceedings of the 5th Information Hiding Workshop, volume 2578 of LNCS, pages 310-323. Springer, 2002.
[25] J. Fridrich, M. Goljan, D. Hogea. Attacking the outguess. In Proceedings of 2002 ACM Workshop on Multimedia and Security, pages 3-6. ACM Press, 2002.
[26] R. BÄohme, A. Westfeld. Breaking cauchy model-based jpeg steganography with first order statistics. In Proceedings of the 9th European Symposium On Research in Computer Security, volume 3193 of LNCS, pages 125-140. Springer, 2004.
[27] Bin Li, Yun Q. Shi, Jiwu Huang. Steganalysis of yass. In Proceedings of the 10th ACM workshop on Multimedia and security (MM&Sec'08), pages 139-148. ACM Press, 2008.
[28] X. Y. Luo, D. S.Wang, P.Wang, F. L. Liu. A review on blind detection for image steganography. Signal Processing, 88(9):2138-2157, 2008.
[29] I. Avcibas, N. Memon, B. Sankur. Steganalysis using image quality metrics. IEEE Transactions on Image Processing, 12(2):221-229, 2003.
[30] J. Fridrich. Feature-based steganalysis for jpeg images and its implications for future design of steganographic schemes. In Proceedings of the 6th Information Hiding Workshop, volume 3200 of LNCS, pages 67-81. Springer, 2004.
[31] Y. Q. Shi, C. Chen, W. Chen. A morkov process based approach to effective attacking jpeg steganography. In Proceedings of the 8th Information Hiding Workshop, volume 4437 of LNCS, pages 249-264. Springer, 2006.
[32] Tomas Pevny, Jessica Fridrich. Merging markov and dct features for multi-class jpeg steganalysis. In Proceedings of SPIE: Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents IX, volume 6505, pages 3-14. SPIE, 2007.
[33] Lyu Siwei, H. Farid. Detecting hidden message using higher-order statistics and support vector machines. In Proceedings of the 5th Information Hiding Workshop, volume 2578 of LNCS, pages 131-142. Springer, 2002.
[34] K. Sullivan, U. Madhow, S. Chandrasekaran, B. S. Manjunath. Steganalysis for markov cover data with applications to images. IEEE Transactions on Information Forensics and Security, 1(2):275-287, 2006.
[35] Zhuo Li, Kuijun Lu, Xianting Zeng, Xuezeng Pan. "A Blind Steganalytic Scheme Based on DCT and Spatial Domain for JPEG Images", Journal of Multimedia, p.200-207, VOL. 5, NO. 3, JUNE 2010.
[36] X. Y. Luo, D. S.Wang, P.Wang, F. L. Liu. A review on blind detection for image steganography. Signal Processing, 88(9):2138-2157, 2008.
[37] McLachlan, Geoffrey J. Discriminant analysis and statistical pattern recognition. Wiley Series in Probability and Mathematical Statistics: Applied Probability and Statistics. A Wiley-Interscience Publication. John Wiley & Sons, Inc., New York, 1992. xvi+526 pp. ISBN: 0-471-61531-5
[38] Corinna Cortes, Vladimir Vapnik."Support-Vector Networks". Machine Leaming, 20, 273-297 (1995) @ 1995 Kluwer Academic Publishers, Boston. Manufactured in The Netherlands.
[39] Bremner D, Demaine E, Erickson J, Iacono J, Langerman S, Morin P, Toussaint G (2005). "Output-sensitive algorithms for computing nearest-neighbor decision boundaries". Discrete and Computational Geometry 33 (4): 593–604.
[40] Bhadeshia H. K. D. H. (1999). "Neural Networks in Materials Science". ISIJ International 39 (10): 966–979.
[41] M. K. Hu, "Visual pattern recognition by moment invariants,” IRE Trans. Information Theory, vol. 8, pp. 179–187, 1962.
[42] Hongli Tian, Huiqiang Yan, Hongdong Zhao. "A Fast and Accurate Approach to the Computation of Zernike Moments" Applied Informatics and Communication Communications in Computer and Information Science. Volume 228, 2011, pp 46-53
[43] Paul S. Dwyer. "The mean and standard deviation of the distribution of group assembly sums". Psychometrika December 1964, Volume 29, Issue 4, pp 397-408
[44] T. Pevny, P. Bas, J. Fridrich Steganalysis by subtractive pixel adjacency matrix. Steganalysis by subtractive pixel adjacency matrix, Princeton, NJ, September 7-8, 2009.
[45] Alfréd Haar, "Zur Theorie der orthogonalen Funktionensysteme", Mathematische Annalen 69 (3): 331–371, (1910), doi: 10.1007/BF01456326.
[46] Daubechies, Ingrid. Ten Lectures on Wavelets. Society for Industrial and Applied Mathematics (1992).
[47] Mahesh S. Chavan, Nikos Mastorakis, Manjusha N. Chavan, M.S. Gaikwad. "Implementation of SYMLET Wavelets to Removal of Gaussian Additive Noise from Speech Signal” Recent Researches in Communications, Automation, Signal Processing, Nanotechnology, Astronomy and Nuclear Physics. P 37-41.
[48] O. Prakash, R. Srivastava; A. Khare. "Biorthogonal wavelet transform based image fusion using absolute maximum fusion rule” IEEE Conference on Information & Communication Technologies (ICT), 2013.
[49] V. Suresh Babu, P. Viswanath. Rough-fuzzy weighted k-nearest leader classifier for large data sets. Pattern Recognition, 42(2009):1719–1731, 2009.
[50] John G. Cleary, Leonard E. Trigg: K*: An Instance-based Learner Using an Entropic Distance Measure. In: 12th International Conference on Machine Learning, 108-114, 1995.
[51] Christopher G. Atkeson, Andrew W. Moore, Stefan Schaal, "LocallyWeighted Learning” Artificial Intelligence Review 11: 11–73, 1997. Kluwer Academic Publishers. Printed in the Netherlands.
[52] Caruana, R.; Niculescu-Mizil, A. (2006). "An empirical comparison of supervised learning algorithms". Proceedings of the 23rd international conference on Machine learning. CiteSeerX: 10.1.1.122.5901.
[53] Rosenblatt, Frank. x. Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. Spartan Books, Washington DC, 1961.
[54] Deng,H.; Runger, G.; Tuv, E. (2011). "Bias of importance measures for multi-valued attributes and solutions". Proceedings of the 21st International Conference on Artificial Neural Networks (ICANN).
[55] P. Pudil, J. Novovicova, J. Kittler, "Floating Search Methods in Feature Selection,” Pattern Recognition Letters,vol. 15, no. 11, pp. 1119 - 1125, November 1994.
[56] A.Brown,S-ToolsVersion4.0,Copyright©1996, http://members.tripod.com/steganography/stego/s-tools4
[57] J. Fridrich, M. Goljan, D. Soukal, Perturbed quantization steganography with wet paper codes," ACM Multimedia Workshop, Magdeburg, Germany, September 20-21, 2004.
[58] P. Sallee, Model-based steganography," International Workshop on Digital Watermarking, Seoul, Korea.2003
[59] Wetfeld, F5a steganographic algorithm: High capacity despite better steganalysis," 4th International Workshop on Information Hiding. , 2001.