Audio Steganalysis Based on Lossless Data-Compression Techniques

  • Fatiha Djebbar
  • Beghdad Ayad
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7618)


In this paper, we introduce a new blind steganalysis method that can reliably detect modifications in audio signals due to steganography. Lossless data-compression ratios are computed from the testing signals and their reference versions and used as features for the classifier design. Additionally, we propose to extract additional features from different energy parts of each tested audio signal to retrieve more informative data and enhance the classifier capability. Support Vector Machine (SVM) is employed to discriminate between the cover- and the stego-audio signals. Experimental results show that our method performs very well and achieves very good detection rates of stego-audio signals produced by S-tools4, Steghide and Hide4PGP.


audio steganalysis active speech level lossless data-compression 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cristianini, N., Shawe-Taylor, J.: An introduction to Support Vector Machines Cambridge University Press (2000)Google Scholar
  2. 2.
    Johnson, K.M., Lyu, S., Farid, H.: Steganalysis of Recorded Speech. In: Proceedings of the Conference on Security, Steganography and Watermarking of Multimedia (SPIE), San Jose, USA, pp. 664–672 (January 2005)Google Scholar
  3. 3.
    Ozer, H., Avcibag, I., Sankur, B., et al.: Steganalysis of Audio Based on Audio Quality Metrics. In: Proceedings of SPIE, Santa Clara, CA, USA, pp. 55–66 (June 2003)Google Scholar
  4. 4.
    Rencher, A.C.: Methods of Multivariate Data Analysis, 2nd edn. John Wiley (2002)Google Scholar
  5. 5.
    Pudil, P., Novovicova, J., Kittler, J.: Floating Search Methods in Feature Selection. Pattern Recognition Letters, 1119–1125 (November 1994)Google Scholar
  6. 6.
    Avcibas: Audio steganalysis with content independent distortion measures. IEEE Signal Process Letter 13(2), 92–95 (2006)CrossRefGoogle Scholar
  7. 7.
    Qi, Y., Fu, J., Yuan, J.: Wavelet domain audio steganalysis based on statistical moments of histogram. Journal of System Simulation 20(7), 1912–1914 (2008)Google Scholar
  8. 8.
    Xuan, G., Shi, Y.Q., Gao, J., et al.: Steganalysis based on multiple features formed by statistical moments of wavelet characteristic functions. In: Proceeding of Information Hiding Workshop, pp. 262–277 (2005)Google Scholar
  9. 9.
    Ru, X., Zhang, H., Huang, X.: Steganalysis of Audio: Attaching the Steghide. In: Proceeding of the Fourth International Conference on Machine Learning and Cybernetics, pp. 3937–3942 (2005)Google Scholar
  10. 10.
    Liu, Q., Sung, A., Qiao, M.: Detecting information hiding in WAV audios. In: Proceeding of 19th International Conference on Pattern Recognition, pp. 1–4 (2008)Google Scholar
  11. 11.
    Liu, Q., Sung, A.H., Qiao, M.: Temporal derivative-based spectrum and mel-cepstrum audio steganalysis. IEEE Transactions on Information Forensics and Security 4(3), 359–368 (2009)CrossRefGoogle Scholar
  12. 12.
    Kraetzer, C., Dittmann, J.: Pros and Cons of Mel-cepstrum Based Audio Steganalysis Using SVM Classification. In: Furon, T., Cayre, F., Doërr, G., Bas, P. (eds.) IH 2007. LNCS, vol. 4567, pp. 359–377. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  13. 13.
    Kraetzer, C., Dittmann, J.: Mel-cepstrum based steganalysis for voip-steganography. In: Proceedings of SPIE, Security, Steganography, and Watermarking of Multimedia Contents IX, vol. 6505, pp. 650505.1–650505.12 (2006)Google Scholar
  14. 14.
    Liu, Q., Sung, A.H., Qiao, M.: Novel Stream Mining for Audio Steganalysis. In: Proceedings of the 17th ACM International Conference on Multimedia, Beijing, China, pp. 95–104 (October 2009)Google Scholar
  15. 15.
    Qi, Y., Wang, Y., Yuan, J.: Audio Steganalysis Based on Co-occurrence Matrix and PCA. In: International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), vol. 1, pp. 433–436 (2009)Google Scholar
  16. 16.
    Liu, Y., Chiang, K., Corbett, C., Archibald, R., Mukherjee, B., Ghosal, D.: A Novel Audio Steganalysis based on Higher-Order Statistics of a Distortion Measure with Hausdorff Distance. LNCS, pp. 487–501 (September 2008)Google Scholar
  17. 17.
    Huttenlocher, D.P., Klanderman, G.A., Rucklidge, W.J.: Comparing Images using the Hausdorff Distance. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(9), 850–863 (1993)CrossRefGoogle Scholar
  18. 18.
    Ru, X., Zhang, Y., Wu, F.: Audio steganalysis based on negative resonance phenomenon caused by steganographic tools. Journal of Zhejiang 7(4), 577–583 (2006)zbMATHCrossRefGoogle Scholar
  19. 19.
    ITU-T Recommendation P56, Telephone Transmission Quality: Objective Measuring Apparatus (March 1996)Google Scholar
  20. 20.
  21. 21.
  22. 22.
  23. 23.
    Vapnik, V.: Statistical Learning Theory. Wiley, Hoboken (1998)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Fatiha Djebbar
    • 1
  • Beghdad Ayad
    • 2
  1. 1.UAE UniversityUAE
  2. 2.Canadian University in DubaiUAE

Personalised recommendations