Journal of the Institute of Electronics and Information Engineers (전자공학회논문지)

The Institute of Electronics and Information Engineers (대한전자공학회)
권호 표지
DOI QR코드

DOI QR Code

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

DCT 계수의 마코프 특징을 이용한 내용 적응적 스테가노그래피의 스테그분석

  • 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)
  • 박태희 (동명대학교 메카트로닉스공학과) ;
  • 한종구 (부산대학교 전자공학과) ;
  • 엄일규 (부산대학교 전자공학과)
  • Received : 2015.03.02
  • Accepted : 2015.07.25
  • Published : 2015.08.25
Download PDF
⟨ Previous Next ⟩

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.

내용 적응적 스테가노그래피는 복잡한 텍스쳐 또는 잡음 영역과 같이 통계적 모델로는 기술하기 어려운 영역에 비밀 메시지를 은닉한다. 이러한 메시지를 검출하기 위해서는 인접 화소간의 국부적인 의존성을 정교하게 모델링해야 하기 때문에 종종 고차원의 특징벡터 추출이 필요하다. 이러한 스테그분석 방법은 계산량이 많을 뿐만 아니라 비밀 메시지의 검출 정확도가 은닉 영역과 사용된 왜곡 척도에 의존한다는 문제점을 가진다. 본 논문에서는 적은 수의 특징 벡터를 이용하여 비밀 메시지의 검출율을 높일 수 있는 개선된 내용 적응적 스테가노그래피의 스테그분석 방법을 제안하고자 한다. 먼저 이산 코사인 변환 계수의 차이를 이용한 특징이 내용 적응적 스테가노그래피의 분석에 유용함을 보이고, 이에 대한 1차 마코프 확률을 특징으로 사용하는 방법을 제시한다. 추출된 특징 벡터는 앙상블 분류기로 입력되어 커버 영상과 스테고 영상을 분류하기 위해 학습된다. 실험 결과 내용 기반 적응적 스테고 영상들에 대해 적은 수의 특징 벡터를 사용함에도 불구하고 기존의 방법에 비해 검출율과 정확도가 우수함을 확인할 수 있었다.

Keywords

References

  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. https://doi.org/10.1109/5.771065
  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. https://doi.org/10.1016/j.sigpro.2009.08.010
  3. A. Nissar, and A. H. Mir, "Classification of steganalysis techniques: A study", Digital Signal Processing, vol. 20, no, 6, pp. 1758-1770, 2010. https://doi.org/10.1016/j.dsp.2010.02.003
  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. https://doi.org/10.1109/JSAC.2011.110807
  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. https://doi.org/10.1016/j.compeleceng.2012.09.008
  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. https://doi.org/10.1016/j.ins.2007.08.007
  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. https://doi.org/10.1109/TIFS.2010.2045842
  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.. https://doi.org/10.1186/1687-417X-2014-1
  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. https://doi.org/10.1109/TIFS.2011.2175919
  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. 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. https://doi.org/10.1109/TIFS.2013.2286682
  18. 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.
  19. NRCS Photo Gallery, http://photogallery.nrcs.usda.gov/res/sites/photogallery
  20. Bossbase1.01, http://www.agents.cz/boss
  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.