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Steganalysis of Perturbed Quantization Using HBCL Statistics and FR Index

  • Veena H. Bhat
  • S. Krishna
  • P. Deepa Shenoy
  • K. R. Venugopal
  • L. M. Patnaik
Part of the Communications in Computer and Information Science book series (CCIS, volume 141)

Abstract

Targeted steganalysis aims at detecting hidden data embedded by a particular algorithm without any knowledge of the ‘cover’ image. In this paper we propose a novel approach for detecting Perturbed Quantization Steganography (PQ) by HFS (Huffman FR index Steganalysis) algorithm using a combination of Huffman Bit Code Length (HBCL) Statistics and File size to Resolution ratio (FR Index) which is not yet explored by steganalysts. JPEG images spanning a wide range of sizes, resolutions, textures and quality are used to test the performance of the model. In this work we evaluate the model against several classifiers like Artificial Neural Networks (ANN), k-Nearest Neighbors (k-NN), Random Forests (RF) and Support Vector Machines (SVM) for steganalysis. Experiments conducted prove that the proposed HFS algorithm can detect PQ of several embedding rates with a better accuracy compared to the existing attacks.

Keywords

Steganography classifiers Huffman coding perturbed quantization 

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References

  1. 1.
    Birgit, P.: Information hiding terminology-results of an informal plenary meeting and additional proposals. In: The Proceedings of the First International Workshop on Information Hiding, pp. 347–350 (1996)Google Scholar
  2. 2.
    Miche, Y., Bas, P., Lendasse, A., Jutten, C., Simula, O.: Reliable steganalysis using a minimum set of samples and features. EURASIP Journal of Information Security, 350–354 (2009)Google Scholar
  3. 3.
    Fridrich, J., Goljan, M., Soukal, D.: Perturbed quantization steganography. ACM Multimedia and Security Journal 11(2), 98–107 (2005)CrossRefGoogle Scholar
  4. 4.
    Fridrich, J., Goljan, M., Soukal, D.: Perturbed quantization steganography using wet paper codes. In: The Proceedings of ACM MM&S Workshop, Germany, pp. 4–15 (2004)Google Scholar
  5. 5.
    Fridrich, J., Pevny, T., Kodovsky, J.: Statistically undetectable JPEG steganography: dead ends. Challenges, and Opportunities. In: The Proceedings of ACM MM&S Workshop, Dallas, TX, pp. 3–14 (2007)Google Scholar
  6. 6.
    Hedieh, S., Mansour, J.: CBS: contourlet-based steganalysis method. Journal of Signal Processing Systems (2010)Google Scholar
  7. 7.
    Kharrazi, M., Sencar, H.T., Memon, N.: Benchmarking steganographic and steganalysis techniques. Journal of Electronic Imaging 15(4) (2006)Google Scholar
  8. 8.
    Zuzana, O., Jiri, H., Ivan, Z., Roman, S.: Steganography detection by means of neural network. In: The Ninteenth International Conference on Database and Expert Systems Application, pp. 571–574. IEEE Computer Society, Los Alamitos (2008)Google Scholar
  9. 9.
    Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  10. 10.
    Chiew, K.L., Pieprzyk, J.: Blind steganalysis: a countermeasure for binary image steganography. In: The Proceedings of the International Conference on Availability, Reliability and Security, pp. 653–658 (2010)Google Scholar
  11. 11.
    Dautrich, J.: Multi-class steganalysis. Machine Learning Course Research Project Distinguishing Images Embedded using Reversible Steganographic Schemes (2009)Google Scholar
  12. 12.
    Yoan, M., Benoit, R., Amaury, L., Patrick, B.: A feature selection methodology for steganalysis. In: Gunsel, B., Jain, A.K., Tekalp, A.M., Sankur, B. (eds.) MRCS 2006. LNCS, vol. 4105, pp. 49–56. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  13. 13.
    Bosch, A., Zisserman, A., Munoz, X.: Image classification using random forests and ferns. In: The IEEE Eleventh International Conference on Computer Vision, pp. 1–8 (2007)Google Scholar
  14. 14.
    Bhat, V.H., Krishna, S., Shenoy, P.D., Venugopal, K.R., Patnaik, L.M.: HUBFIRE - A multi-class SVM based JPEG steganalysis using HBCL statistics and FR index. In: SECRYPT (in press, 2010) Google Scholar
  15. 15.
    Kharrazi., M., Sencar., H.T., Memon, N.: A performance study of common image steganography and steganalysis techniques. Journal of Electronic Imaging 15(4) (2006)Google Scholar
  16. 16.
    Fawcett, T.: ROC graphs: notes and practical considerations for data mining researchers. Technical Report HPL-2003–4. HP Laboratories, Palo Alto (2003)Google Scholar
  17. 17.
    Bishop, C.: Neural networks for pattern recognition. Oxford, Oxford University, UK (1995)zbMATHGoogle Scholar
  18. 18.
    Ho, T.: Random decision forest. In: The Third International Conference on Document Analysis and Recognition, pp. 278–282 (1995)Google Scholar
  19. 19.
  20. 20.
    Burges, C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)CrossRefGoogle Scholar
  21. 21.
    Gül, G., Dirik, A.E., Avcıbas, I.: Steganalytic features for JPEG compression-based perturbed quantization. IEEE Signal Processing Letters 14(3), 205–208 (2007)CrossRefGoogle 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.
    Avcibas, I., Kharrazi, M., Memon, N., Sankur, B.: Image steganalysis with binary similarity measures. EURASIP Journal on Applied Signal Processing, 2749–2757 (2005)Google Scholar
  24. 24.
    Lyu., S., Farid, H.: Steganalysis using color wavelet statistics and one-class support vector machines. In: SPIE Symposium on Electronic Imagining, San Jose, CA (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Veena H. Bhat
    • 2
  • S. Krishna
    • 1
  • P. Deepa Shenoy
    • 1
  • K. R. Venugopal
    • 1
  • L. M. Patnaik
    • 3
  1. 1.Department of Computer Science and EngineeringUniversity Visvesvaraya College of EngineeringBangaloreIndia
  2. 2.IBS Bangalore, Bangalore UniversityIndia
  3. 3.Defence Institute of Advanced TechnologyPuneIndia

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