Browse > Article
http://dx.doi.org/10.5573/ieie.2015.52.8.097

Steganalysis of Content-Adaptive Steganography using Markov Features for DCT Coefficients  

Park, Tae Hee (Dept. Mechatronics Eng., TongMyong University)
Han, Jong Goo (Dept. Electronics Eng., Pusan National University)
Eom, Il Kyu (Dept. Electronics Eng., Pusan National University)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.52, no.8, 2015 , pp. 97-105 More about this Journal
Abstract
Content-adaptive steganography methods embed secret messages in hard-to-model regions of covers such as complicated texture or noisy area. Content-adaptive steganalysis methods often need high dimensional features to capture more subtle relationships of local dependencies among adjacent pixels. However, these methods require many computational complexity and depend on the location of hidden message and the exploited distortion metrics. In this paper, we propose an improved steganalysis method for content-adaptive steganography to enhance detection rate with small number features. We first show that the features form the difference between DCT coefficients are useful for analyzing the content-adaptive steganography methods, and present feature extraction mehtod using first-order Markov probability for the the difference between DCT coefficients. The extracted features are used as input of ensemble classifier. Experimental results show that the proposed method outperforms previous schemes in terms of detection rates and accuracy in spite of a small number features in various content-adaptive stego images.
Keywords
content-adaptive steganography; DCT coefficient; Markov chain; difference image; ensemble classifier;
Citations & Related Records
연도 인용수 순위
  • Reference
1 F. A. P. Petitcolas, R. J. Anderson, and M. G. Kuhn, "Information hiding - a survey," Proceedings of the IEEE, vol. 87, no. 7, pp. 1062-1078, 1999.   DOI   ScienceOn
2 C. Abbas, C. Joan, C. Kevin and M. Paul, "Digital image steganography : Survey and analysis of current methods," Signal Processing, vol. 90, no. 3, pp. 727-752, 2010.   DOI   ScienceOn
3 A. Nissar, and A. H. Mir, "Classification of steganalysis techniques: A study", Digital Signal Processing, vol. 20, no, 6, pp. 1758-1770, 2010.   DOI   ScienceOn
4 H. Farid, S. Lyu, "Detecting hidden messages using higher order statistics and support vector machines", Lecture Notes in Computer Science, vol. 2578, pp. 340-354, 2002.
5 Y. Wang, P. Moulin, "Optimized feature extraction for learning-based image steganalysis," IEEE Transactions on Image Processing, vol. 2, no. 1, pp. 31-45, 2007.
6 X. Luo, F. Liu, S. Lian, C. Yang, S. Gritzalis, "On the typical statistic features for image blind steganalysis," IEEE Journal on Selected Areas in Communications, vol. 29, no. 7, pp. 1404-1422, 2011.   DOI   ScienceOn
7 M. Tang, J. Hu, M. Fan and W. Song, "A steganalysis by adjacency pixel bits structure," Computers and Electrical Engineering, vol. 39, no. 2, pp. 488-498. 2013.   DOI   ScienceOn
8 Q. Liu, A. H. Sung, B. Ribeiro, M. Wei, Z. Chen, J. Xu, "Image complexity and feature mining for steganalysis of least significant bit matching steganography," Information Sciences, vol. 178, pp. 21-36, 2008.   DOI   ScienceOn
9 T. H. Park, S. H. Hyun, J. H. Kim and I. K. Eom, "Steganalysis using joint moment of wavelet subbands," Journal of IEEK, vol. 48-SP, no. 3, pp. 71-78, 2011.
10 T. Pevny, P. Bas and J. Fridrich, "Steganalysis by subtractive pixel adjacency matrix," IEEE transaction on Information Forensics and Security. vol. 5, no. 2, pp. 215-224, 2010.   DOI   ScienceOn
11 T. Pevny, T. Filler, and P. Bas, "Using high-dimensional image models to perform highly undetectable steganography," Lecture Notes in Computer Science, vol. 6387, pp. 161-177, 2010.
12 V. Holub and J. Fridrich, "Digital image steganography using universal distortion." Proceedings of the first ACM workshop on Information hiding and multimedia security, pp. 59-68, 2013.
13 V. Holub, J. Fridrich and T. Denemark, "Universal distortion function for steganography in an arbitrary domain," EURASIP Journal on Information Security, vol. 2014:1, pp. 1-13, 2014..   DOI   ScienceOn
14 J. Kodovsky, J. Fridrich, and V. Holub, "Ensemble classifiers for steganalysis of digital media," IEEE Transaction on Information Forensics and Security. vol. 7, no. 2, pp. 432-444, 2012.   DOI   ScienceOn
15 Q. Liu, "Steganalysis of DCT-embedding based adaptive steganography and YASS," Proceedings of the thirteenth ACM Multimedia Workshop on Multimedia and Security, pp. 77-86), 2011.
16 J. Fridrich, J. Kodovsky, V. Holub, and M. Goljan, "Steganalysis of content-adaptive steganography in spatial domain," Lecture Notes in Computer Science, vol. 6958, pp. 102-117, 2011.
17 Bossbase1.01, http://www.agents.cz/boss
18 V. Holub, J. Fridrich, "Random projections of residuals for digital image seganalysis," IEEE Transactions on Information Forensics and Security, vol. 8, no. 12, pp. 1996-2006, 2013.   DOI   ScienceOn
19 T. Denemark, J. Fridrich and V. Holub, "Further Study on the Security of S-UNIWARD," Proceedings of SPIE Media Watermarking, Security, and Forensics, vol. 9028, pp. p. 902805-1-902805-13, 2014.
20 NRCS Photo Gallery, http://photogallery.nrcs.usda.gov/res/sites/photogallery
21 I. H. Witten, and E. Frank, Data Mining, Elservier, 2005.
22 A. Westfeld, "ROC curves for steganalysis," Proceedings of the third WAVILA Challenge, pp. 39-45, 2007.