Effect of Different Coding Patterns on Compressed Frequency Domain Based Universal JPEG Steganalysis

  • Bin Li
  • Fangjun Huang
  • Shunquan Tan
  • Jiwu Huang
  • Yun Q. Shi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5041)


Current steganalytic schemes for JPEG steganography are in favor of extracting features in DCT (Discrete Cosine Transform) domain and/or spatial domain. A recently proposed compressed frequency domain based universal steganalytic algorithm [21] showed concern over the statistics of Huffman compressed stream. The authors claimed that it was very effective in detecting stego images embedded by JPEG steganographic tools, including JPHide, F5 and OutGuess. Even though only several bytes were embedded, the scheme was still able to work, thus demonstrating astonishing steganalysis capability. By carefully controlled studies on the factors which may have impact on the new steganalytic method, we find out the truly cause of the powerfulness of this “payload-independent” steganalyzer. Experimental results reveal that different coding patterns used in cover and stego images, rather than the Avalanche Criterion [24] explained by its authors, have led to great detection efficiency. When the same coding pattern is employed in both cover and stego images, the performance of the newly devised steganalyzer has greatly dropped. Implication from this paper is that we should ensure the difference between the cover and stego images is only caused by data embedding itself in steganography and steganalysis.


Discrete Cosine Transform Cover Image JPEG Compression Stego Image Code Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
  2. 2.
    Latham, A.: Steganography: JPHIDE and JPSEEK (1999),
  3. 3.
    Westfeld, A.: F5 – A steganographic algorithm (high capacity despite better steganalysis). In: Moskowitz, I.S. (ed.) IH 2001. LNCS, vol. 2137, pp. 289–302. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  4. 4.
    Provos, N.: Defending against statistical steganalysis. In: 10th USENIX Security Symposium (2001)Google Scholar
  5. 5.
    Sallee, P.: Model-based steganography. In: Kalker, T., Cox, I., Ro, Y.M. (eds.) IWDW 2003. LNCS, vol. 2939, pp. 154–167. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Sallee, P.: Model-based methods for steganography and steganalysis. International Journal of Image and Graphics 5, 167–189 (2005)CrossRefGoogle Scholar
  7. 7.
    Fridrich, J., Goljan, M., Soukal, D.: Perturb quantization steganography using wet paper codes. In: Dittman, J., Fridrich, J. (eds.) Proceedings ACM Multimedia and Security Workshop, pp. 4–15. ACM Press, New York (2004)Google Scholar
  8. 8.
    Kim, Y., Duric, Z., Richards, D.: Modified matrix encoding for minimal distortion steganography. In: Information Hiding: 8th International Workshop (2006)Google Scholar
  9. 9.
    Westfeld, A., Pfitzmann, A.: Attacks on steganographic systems. In: Pfitzmann, A. (ed.) IH 1999. LNCS, vol. 1768, pp. 61–75. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  10. 10.
    Provos, N., Honeyman, P.: Detecting steganographic content on the Internet. CITI Technical Report, pp. 01–11 (2001)Google Scholar
  11. 11.
    Westfeld, A.: Detecting low embedding rates. In: Petitcolas, F.A.P. (ed.) IH 2002. LNCS, vol. 2578, pp. 324–339. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  12. 12.
    Fridrich, J., Goljan, M., Hogea, D.: New methodology for breaking steganographic techniques for JPEGs. In: Delp III, E.J., Wong, P.W. (eds.) Proceedings SPIE, Electronic Imaging, Security, Steganography of Multimedia Contents V, San Jose, CA, January 20-24, vol. 5020, pp. 143–155 (2003)Google Scholar
  13. 13.
    Fridrich, J., Goljan, M., Hogea, D.: Steganalysis of JPEG images: Breaking the F5 algorithm. In: Petitcolas, F.A.P. (ed.) IH 2002. LNCS, vol. 2578, pp. 310–323. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  14. 14.
    Fridrich, J., Goljan, M., Hogea, D.: Attacking the OutGuess. In: Proceedings ACM Workshop on Multimedia and Security (2002)Google Scholar
  15. 15.
    Böhme, R., Westfeld, A.: Breaking Cauchy model-based JPEG steganography with first order statistic. In: Samarati, P., Ryan, P.Y.A., Gollmann, D., Molva, R. (eds.) ESORICS 2004. LNCS, vol. 3193, pp. 125–140. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  16. 16.
    Farid, H.: Detecting steganographic messages in digital images. Technical Report, TR2001-412, Dartmouth College, Computer Science (2001)Google Scholar
  17. 17.
    Avcibas, I., Memon, N., Sankur, B.: Image steganalysis with binary similarity measures. In: Proceedings of the IEEE International Conference on Image Processing, Rochester, New York, vol. 3, pp. 45–648 (2002)Google Scholar
  18. 18.
    Fridrich, J.: Feature-based steganalysis for JPEG images and its implications for future design of steganographic schemes. In: Fridrich, J. (ed.) IH 2004. LNCS, vol. 3200, pp. 67–81. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  19. 19.
    Shi, Y.Q., Chen, C., Chen, W.: A Markov process based approach to effective attacking JPEG steganography. In: 8th Information Hiding Workshop (2006)Google Scholar
  20. 20.
    Pevny, T., Fridrich, J.: Merging Markov and DCT features for multi-class JPEG steganalysis. In: Proc. SPIE Electronic Imaging, Photonics West (2007)Google Scholar
  21. 21.
    Barbier, J., Filiol, E., Mayoura, K.: Universal JPEG steganalysis in the compressed frequency domain. In: Shi, Y.Q., Jeon, B. (eds.) IWDW 2006. LNCS, vol. 4283, pp. 253–267. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  22. 22.
    Barbier, J., Filiol, E., Mayoura, K.: New features for specific JPEG Steganalysis. Transactions on Engineering, Computing and Technology 16, 72–77 (2006)Google Scholar
  23. 23.
    Wallace, G.K.: The JPEG still picture compression standard. Communication of ACM 34(4), 30–44 (1991)CrossRefGoogle Scholar
  24. 24.
    Feistel, H.: Cryptography and computer privacy. Scientific American 228(5), 15–23 (1973)CrossRefGoogle Scholar
  25. 25.
    Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley Interscience, New York (1991)CrossRefzbMATHGoogle Scholar
  26. 26.
    Schaefer, G., Stich, M.: UCID – An uncompressed colour image database. In: Proceedings SPIE, Storage and Retrieval Methods and Applications for Multimedia, San Jose, USA, pp. 472–480 (2004)Google Scholar
  27. 27.
    Sallee, P.: Matlab JPEG Toolbox (2003),
  28. 28.
    Kharrazi, M., Sencar, H.T., Memon, N.: Benchmarking steganographic and steganalysis techniques. In: Delp III, E.J., Wong, P.W. (eds.) Proceedings SPIE Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents VII, January 16-20, vol. 5681, pp. 252–263 (2005)Google Scholar
  29. 29.
    Shi, Y.Q., Chen, C., Chen, W., Kaundinya, M.P.: Effect of recompression on attacking JPEG steganographic schemes – An experimental study. In: IEEE International Symposium on Circuits and Systems (2007)Google Scholar
  30. 30.
    Westfeld, A.: Steganalysis in the presence of weak cryptography and encoding. In: Shi, Y.Q., Jeon, B. (eds.) IWDW 2006. LNCS, vol. 4283, pp. 19–34. Springer, Heidelberg (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Bin Li
    • 1
    • 2
  • Fangjun Huang
    • 1
  • Shunquan Tan
    • 1
  • Jiwu Huang
    • 1
  • Yun Q. Shi
    • 2
  1. 1.School of Information Science and TechnologySun Yat-sen UniversityGuangzhouChina
  2. 2.New Jersey Institute of TechnologyNewarkUSA

Personalised recommendations