Multi-class Blind Steganalysis Based on Image Run-Length Analysis

  • Jing Dong
  • Wei Wang
  • Tieniu Tan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5703)


In this paper, we investigate our previously developed run-length based features for multi-class blind image steganalysis. We construct a Support Vector Machine classifier for multi-class recognition for both spatial and frequency domain based steganographic algorithms. We also study hierarchical and non-hierarchical multi-class schemes and compare their performance for steganalysis. Experimental results demonstrate that our approach is able to classify different stego images according to their embedding techniques based on appropriate supervised learning. It is also shown that the hierarchical scheme performs better in our experiments.


blind steganalysis multi-class image steganalysis run-length analysis 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Fridrich, J., Goljan, M., Hogea, D., Soukal, D.: Quantitative steganalysis: Estimating secret message length. ACM Multimedia Systems Journal. Special issue on Multimedia Securrity 9(3), 288–302 (2003)CrossRefGoogle Scholar
  2. 2.
    Dumitrescu, S., Xiaolin, W., Wang, Z.: Detection of lsb steganography via sample pair analysis. In: Petitcolas, F.A.P. (ed.) IH 2002. LNCS, vol. 2578, pp. 355–374. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  3. 3.
    Ker, A.: Resampling and the detection of lsb matching in colour bitmaps. In: Proceedings of SPIE Electronic Imaging, Security, Steganography and Watermarking of Multimedia Contents VII (2005)Google Scholar
  4. 4.
    Farid, H., Siwei, L.: Detecting hidden messages using higher-order statistics and support vector machines. In: Petitcolas, F.A.P. (ed.) IH 2002. LNCS, vol. 2578, pp. 340–354. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  5. 5.
    Fridrich, J., Goljan, M.: Practical steganalysis of digital images — state of the art. In: Security and Watermarking of Multimedia Contents. SPIE, vol. 4675, pp. 1–13 (2002)Google Scholar
  6. 6.
    Harmsen, J.J., Pearlman, W.A.: Steganalysis of additive noise modelable information hiding. In: Proc. SPIE, Security, Steganography, and Watermarking of Multimedia Contents VI, pp. 131–142 (2003)Google Scholar
  7. 7.
    Shi, Y.Q., et al.: Image steganalysis based on moments of characteristic functions using wavelet decomposition, prediction-error image, andneural network. In: ICME 2005, pp. 269–272 (2005)Google Scholar
  8. 8.
    Dong, J., Tan, T.N.: Blind image steganalysis based on run-length histogram analysis. In: 15th International Conference of Image Processing 2008 (ICIP 2008), pp. 2064–2067 (2008)Google Scholar
  9. 9.
    Pevny, T., Fridrich, J.: Towards muti-class steganalyzer for jpeg images. In: Barni, M., Cox, I., Kalker, T., Kim, H.-J. (eds.) IWDW 2005. LNCS, vol. 3710, pp. 39–53. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Salle, 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
  11. 11.
    Pevny, T., Fridrich, J.: Determining the stego algorithm for jpeg images. In: Proceddings of Information Security, vol. 153, pp. 77–86 (2006)Google Scholar
  12. 12.
    Shi, Y.Q., Chen, C., Chen, W.: A markov process based approach to effective attacking jpeg steganography. In: Camenisch, J.L., Collberg, C.S., Johnson, N.F., Sallee, P. (eds.) IH 2006. LNCS, vol. 4437, pp. 249–264. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  13. 13.
    Savoldi, A., Gubian, P.: A markov process based approach to effective attacking jpeg steganography. In: Proceedings of the Third International Conference on International Information Hiding and Multimedia Signal Processing, vol. 2, pp. 93–96 (2007)Google Scholar
  14. 14.
    Westfeld, A.: High capacity despite better steganalysis(f5). In: Moskowitz, I.S. (ed.) IH 2001. LNCS, vol. 2137, pp. 289–302. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  15. 15.
    Provos, N.: Software,
  16. 16.
  17. 17.
    Hetzl, S.: Software,
  18. 18.
    Wang, P., Liu, F., Wang, G., Sun, Y., Gong, D.: Multi-class steganalysis for jpeg stego algorithms. In: Proceedings of the 15th International Conference on Image Processing, pp. 2076–2079 (2008)Google Scholar
  19. 19.
    Galloway, M.M.: Texture analysis using gray level run lengths. In: Cornput. Graph. Image Proc., vol. 4, pp. 171–179 (1975)Google Scholar
  20. 20.
    Cortes, C., Vapnik, V.: Support-vector network. In: Proceedings of SPIE Electronic Imageing, Security, Steganography and Watermarking of Nultimedia Contents VII, vol. 20, pp. 273–297 (1995)Google Scholar
  21. 21.
    Kharrazi, M., Sencar, H.T., Memon, N.: Benchmarking steganographic and steganalysis techniques. In: Proceedings of SPIE Electronic Imaging, Security, Steganography and Watermarking of Multimedia Contents VII (2005)Google Scholar
  22. 22.
    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
  23. 23.
    Hsu, C., Kin, C.: A comparision of methods for multi-class support vector machines. Technical Report, Department of Computer Science and Information Engineering, National Taiwan University,
  24. 24.
    Schaefer, G., Stich, M.: Ucid - an uncompressed colour image database. In: Proc. SPIE, Storage and Retrieval Methods and Applications for Multimedia, pp. 472–480 (2004)Google Scholar
  25. 25.
  26. 26.
    Cox, I.J., Kilian, J., Leighton, F.T., Shamoon, T.: Secure spread spectru, watermarking for multimedia. IEEE Trans.Image Process. 6(12), 1673–1687 (1997)CrossRefGoogle Scholar
  27. 27.
    Solanki, K., Sarkar, A., Manjunath, B.S.: YASS: Yet another steganographic scheme that resists blind steganalysis. In: Furon, T., Cayre, F., Doërr, G., Bas, P. (eds.) IH 2007. LNCS, vol. 4567, pp. 16–31. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jing Dong
    • 1
  • Wei Wang
    • 1
  • Tieniu Tan
    • 1
  1. 1.National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijing

Personalised recommendations