Skip to main content
SpringerLink
Log in
Book cover

International Workshop on Digital Watermarking

IWDW 2005: Digital Watermarking pp 39–53Cite as

Towards Multi-class Blind Steganalyzer for JPEG Images

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 3710))

Abstract

In this paper, we use the previously proposed calibrated DCT features [9] to construct a Support Vector Machine classifier for JPEG images capable of recognizing which steganographic algorithm was used for embedding. This work also constitutes a more detailed evaluation of the performance of DCT features as in [9] only a linear classifier was used. The DCT features transformed using Principal Component Analysis enable an interesting visualization of different stego programs in a three-dimensional space. This paper demonstrates that, at least under some simplifying assumptions in which the effects of double compression are ignored, it is possible to reliably classify stego images to their embedding techniques. The classifier is capable of generalizing to previously unseen techniques.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. JP Hide&Seek, http://linux01.gwdg.de/~alatham/stego.html

  2. Anderson, R.J., Petitcolas, F.A.P.: On the limits of steganography. IEEE Journal of Selected Areas in Communications, Special Issue on Copyright and Privacy Protection 16(4), 474–481 (1998)

    Google Scholar 

  3. Avcibas, I., Memon, N., Sankur, B.: Steganalysis using image quality metrics. In: Proceeedings of SPIE Electronic Imaging, Security and Watermarking of Multimedia Contents, San Jose, CA, vol. 4314, pp. 523–531 (2001)

    Google Scholar 

  4. Avcibas, I., Sankur, B., Memon, N.: Image steganalysis with binary similarity measures. In: Proceedings of International Conference on Image Processing, vol. 3, pp. 645–648 (2002)

    Google Scholar 

  5. Cachin, C.: An information-theoretic model for steganography. In: Aucsmith, D. (ed.) IH 1998. LNCS, vol. 1525, pp. 306–318. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  6. Chandramouli, R., Kharrazi, M., Memon, N.: Image steganography and steganalysis. In: Kalker, T., Cox, I., Ro, Y.M. (eds.) IWDW 2003. LNCS, vol. 2939, pp. 25–49. Springer, Heidelberg (2002)

    Google Scholar 

  7. 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)

    Chapter  Google Scholar 

  8. 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)

    Chapter  Google Scholar 

  9. 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)

    Chapter  Google Scholar 

  10. Fridrich, J., Goljan, M., Hogea, D., Soukal, D.: Quantitative steganalysis: Estimating secret message length. ACM Multimedia Systems Journal. Special issue on Multimedia Security 9(3), 288–302 (2003)

    Google Scholar 

  11. Harmsen, J.J., Pearlman, W.A.: Steganalysis of additive noise modelable information hiding. In: Proceedings of SPIE Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents V, Santa Clara, CA, pp. 131–142 (2003)

    Google Scholar 

  12. Hsu, C., Chang, C., Lin, C.: A practical guide to support vector classification. Department of Computer Science and Information Engineering, National Taiwan University, Taiwan, http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf

  13. Hsu, C., Lin, C.: A comparison of methods for multi-class support vector machines. Technical report, Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan (2001), http://citeseer.ist.psu.edu/hsu01comparison.html

  14. Katzenbeisser, S., Petitcolas, F.A.P.: Security in steganographic systems. In: Proceedings of SPIE Electronic Imaging, Security and Watermarking of Multimedia Contents IV, San Jose, CA, vol. 4675, pp. 50–56 (2002)

    Google Scholar 

  15. 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, San Jose, CA (2005) (to appear)

    Google Scholar 

  16. Ker, A.: Steganalysis of LSB matching in grayscale images. IEEE Sig. Proc. Letters (2005) (to appear)

    Google Scholar 

  17. Kharrazi, M., Sencar, H.T., Memon, N.: Benchmarking steganographic and steganalytic techniques. In: Proceedings of SPIE Electronic Imaging, Security, Steganography and Watermarking of Multimedia Contents VII, San Jose, CA (2005) (to appear)

    Google Scholar 

  18. Lyu, S., Farid, H.: Steganalysis using color wavelet statistics and one-class support vector machines. In: Proceedings of SPIE Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents, San Jose, CA, pp. 35–45 (2004)

    Google Scholar 

  19. Platt, J., Cristianini, N., Shawe-Taylor, J.: Large margin DAGs for multiclass classification. In: Solla, S.A., Leen, T.K., Mueller, K.-R. (eds.) Advances in Neural Information Processing Systems, vol. 12, pp. 547–553 (2000)

    Google Scholar 

  20. Provos, N.: Defending against statistical steganalysis. In: 10th USENIX Security Symposium, Washington DC (2001)

    Google Scholar 

  21. 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)

    Chapter  Google Scholar 

  22. Soukal, D., Fridrich, J., Goljan, M.: Maximum likelihood estimation of secret message length embedded using PMK steganography in spatial domain. In: Proceedings of SPIE Electronic Imaging, Security, Steganography and Watermarking of Multimedia Contents VII, San Jose, CA (2005) (to appear)

    Google Scholar 

  23. Tzschoppe, R., Bäuml, R., Huber, J.B., Kaup, A.: Steganographic system based on higher-order statistics. In: Proceedings of SPIE Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents V, Santa Clara, CA, pp. 156–166 (2003)

    Google Scholar 

  24. Westfeld, A.: High capacity despite better steganalysis (F5 a steganographic algorithm). In: Moskowitz, I.S. (ed.) IH 2001. LNCS, vol. 2137, pp. 289–302. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  25. Zöllner, J., Federrath, H., Klimant, H., Pfitzmann, A., Piotraschke, R., Westfeld, A., Wicke, G., Wolf, G.: Modeling the security of steganographic systems. In: Aucsmith, D. (ed.) IH 1998. LNCS, vol. 1525, pp. 344–354. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science,  

    Tomáš Pevný

  2. Department of Electrical and Computer Engineering, SUNY Binghamton, Binghamton, NY, 13902–6000

    Jessica Fridrich

Authors
  1. Tomáš Pevný
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jessica Fridrich
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dipartimento di Ingegneria dell’Informazione, Università degli Studi di Siena, Italy

    Mauro Barni

  2. University College London, Torrington Place, UK

    Ingemar Cox

  3. Hewlett-Packard Laboratories, Palo Alto, CA, USA

    Ton Kalker

  4. Multimedia Security Lab, Korea University, Science Campus, 136-701, Seoul, Korea

    Hyoung-Joong Kim

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Pevný, T., Fridrich, J. (2005). Towards Multi-class Blind Steganalyzer for JPEG Images. In: Barni, M., Cox, I., Kalker, T., Kim, HJ. (eds) Digital Watermarking. IWDW 2005. Lecture Notes in Computer Science, vol 3710. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551492_4

Download citation

  • DOI: https://doi.org/10.1007/11551492_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28768-1

  • Online ISBN: 978-3-540-32052-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation