Advertisement

Prior Classification of Stego Containers as a New Approach for Enhancing Steganalyzers Accuracy

  • Viktor Monarev
  • Andrey PestunovEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9543)

Abstract

We introduce a novel “prior classification” approach which can be employed in order to enhance the accuracy of stego detectors as well as to estimate it more subtly. The prior classification is intended for selection a subset of a testing set with such a property that a detection error, calculated over this subset, may be substantially lower than that calculated over the whole set. Our experiments demonstrated that it is possible to select about 30 % of the BOSSbase images for which HUGO 0.4 bpp is detected with the error less than 0.003, while the error over the whole set is 0.141. We also demonstrated that it is possible to find about 5 % of the BOSSbase images which provide the detection error for HUGO 0.1 bpp less than 0.05, while the error, calculated over the whole set, is about 0.37 which is not quite a reliable accuracy.

Keywords

Information hiding Steganalysis HUGO Prior classification Feature-based steganalysis SRM features Ensemble classifier 

Notes

Acknowledgment

This research has been supported by the Russian Foundation of Basic Research, grant no. 14-01-31484.

References

  1. 1.
    Bas, P., Filler, T., Pevný, T.: Break our steganographic system: the ins and outs of organizing BOSS. In: Filler, T., Pevný, T., Craver, S., Ker, A. (eds.) IH 2011. LNCS, vol. 6958, pp. 59–70. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  2. 2.
    Biryukov, A., Nakahara Jr., J., Preneel, B., Vandewalle, J.: New weak-key classes of IDEA. In: Deng, R.H., Qing, S., Bao, F., Zhou, J. (eds.) ICICS 2002. LNCS, vol. 2513, pp. 315–326. Springer, Heidelberg (2002)Google Scholar
  3. 3.
    Duda, R., Hart, P., Stork, D.: Pattern classification, 2nd edn. Wiley, New York (2001)zbMATHGoogle Scholar
  4. 4.
    Fridrich, J.: Rich models for steganalysis of dugital images. IEEE Trans. Inf. Forensics Secur. 7(3), 868–882 (2012)CrossRefGoogle Scholar
  5. 5.
    Fridrich, J., Kodovský, J., Holub, V., Goljan, M.: Steganalysis of content-adaptive steganography in spatial domain. In: Filler, T., Pevný, T., Craver, S., Ker, A. (eds.) IH 2011. LNCS, vol. 6958, pp. 102–117. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  6. 6.
    Fridrich, J., Kodovský, J., Holub, V., Goljan, M.: Steganalysis of content-adaptive steganography in spatial domain. In: Filler, T., Pevný, T., Craver, S., Ker, A. (eds.) IH 2011. LNCS, vol. 6958, pp. 102–117. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Holub, V., Fridrich, J.: Designing steganographic distortion using directional filters. In: Proceedings 4th IEEE International Workshop on Information Forensics and Security, pp. 234–239. IEEE (2012)Google Scholar
  8. 8.
    Holub, V., Fridrich, J.: Digital image steganography using universal distortion. In: Proceedings 1th ACM Workshop Information Hiding and Multimedia Security, pp. 59–68 (2013)Google Scholar
  9. 9.
    Holub, V., Fridrich, J.: Random projections of residuals for digital image steganalysis. IEEE Trans. Inf. Forensics Secur. 8(12), 1996–2006 (2013)CrossRefGoogle Scholar
  10. 10.
    Kara, O., Manap, C.: A new class of weak keys for blowfish. In: Biryukov, A. (ed.) FSE 2007. LNCS, vol. 4593, pp. 167–180. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  11. 11.
    Ker, A., et al.: Moving steganography and steganalysis from the laboratory into the real world. In: Proceedings 1st ACM Workshop on Information Hiding and Multimedia Security, pp. 45–58. ACM (2013)Google Scholar
  12. 12.
    Kodovsky, J., Fridrich, J., Holub, V.: Ensemble classifiers for steganalysis of digital media. IEEE Trans. Inf. Forensics Secur. 7(2), 434–444 (2011)Google Scholar
  13. 13.
    Monarev, V., Pestunov, A.: A known-key scenario for steganalysis and a highly accurate detector within it. In: Proceedings IEEE 10th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 175–178. IEEE (2014)Google Scholar
  14. 14.
    Pevny, T., Bas, P., Fridrich, J.: Steganalysis by subtractive pixel adjacency matrix. IEEE Trans. Inf. Forensics Secur. 5(2), 215–224 (2010)CrossRefGoogle Scholar
  15. 15.
    Pevny, T.: Detecting messages of unknown length. In: Proceedings 8th Media Watermarking, Security and Forensics, pp. 1–12 (2011)Google Scholar
  16. 16.
    Pevný, T., Filler, T., Bas, P.: Using high-dimensional image models to perform highly undetectable steganography. In: Böhme, R., Fong, P.W.L., Safavi-Naini, R. (eds.) IH 2010. LNCS, vol. 6387, pp. 161–177. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  17. 17.
    Feature Extractors for Steganalysis. http://dde.binghamton.edu/download/feature_extractors/
  18. 18.
    Break our watermarking system 2nd ed. http://bows2.ec-lille.fr/
  19. 19.
    Break our steganographic system. http://www.agents.cz/boss/
  20. 20.

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Novosibirsk State UniversityNovosibirskRussia
  2. 2.Institute of Computational Technologies SB RASNovosibirskRussia
  3. 3.Novosibirsk State University of Economics and ManagementNovosibirskRussia

Personalised recommendations