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Steganalysis Using High-Dimensional Features Derived from Co-occurrence Matrix and Class-Wise Non-Principal Components Analysis (CNPCA)

  • Guorong Xuan
  • Yun Q. Shi
  • Cong Huang
  • Dongdong Fu
  • Xiuming Zhu
  • Peiqi Chai
  • Jianjiong Gao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4283)

Abstract

This paper presents a novel steganalysis scheme with high-dimensional feature vectors derived from co-occurrence matrix in either spatial domain or JPEG coefficient domain, which is sensitive to data embedding process. The class-wise non-principal components analysis (CNPCA) is proposed to solve the problem of the classification in the high-dimensional feature vector space. The experimental results have demonstrated that the proposed scheme outperforms the existing steganalysis techniques in attacking the commonly used steganographic schemes applied to spatial domain (Spread-Spectrum, LSB, QIM) or JPEG domain (OutGuess, F5, Model-Based).

Keywords

steganalysis co-occurrence matrix class-wise non-principal components analysis (CNPCA) 

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References

  1. 1.
    Farid, H.: Detecting hidden messages using higher-order statistical models. In: Proceeding of the IEEE International Conference on Image Processing, New York, vol. II, pp. 905–908 (2002)Google Scholar
  2. 2.
    Xuan, G., Shi, Y.Q., Gao, J., Zou, D., Yang, C., Zhang, Z., Chai, P., Chen, C.-H., Chen, W.: Steganalysis based on multiple features formed by statistical moments of wavelet characteristic functions. In: Barni, M., Herrera-Joancomartí, J., Katzenbeisser, S., Pérez-González, F. (eds.) IH 2005. LNCS, vol. 3727, pp. 262–277. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  3. 3.
    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. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  4. 4.
    Provos, N.: Defending against statistical steganalysis. In: 10th USENIX Security Symposium, Washington DC, USA (2001)Google Scholar
  5. 5.
    Westfeld, A.: F5-A steganographic algorithm. In: Moskowitz, I.S. (ed.) IH 2001. LNCS, vol. 2137, p. 289. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  6. 6.
    Sallee, P.: Model-based methods for steganography and steganalysis. International Journal of Image and Graphics 5(1), 167–190 (2005)CrossRefGoogle Scholar
  7. 7.
    Sullivan, K., Madhow, U., Chandrasekaran, S., Manjunath, B.S.: Steganalysis of spread spectrum data hiding exploiting cover memory. In: SPIE 2005, vol. 5681, pp. 38–46 (2005)Google Scholar
  8. 8.
    Haralick, R.M.: Textural features for image classification. IEEE Trans. Systems Man Cybernetics SMC-3 (1973)Google Scholar
  9. 9.
    Xuan, G., Chai, P., Zhu, X., Yao, Q., Huang, C., Shi, Y.Q., Fu, D.: A novel pattern classification scheme: Classwise non-principal component analysis (CNPCA). In: International Conference on Pattern Recognition (ICPR), Hong Kong (August 2006)Google Scholar
  10. 10.
    Shi, Y.Q., Sun, H.: Image and Video Compression for Multimedia Engineering: Fundamentals, Algorithms, and Standards. CRC Press, Boca Raton (1999)CrossRefGoogle Scholar
  11. 11.
  12. 12.
  13. 13.
    Fawcett, T.: ROC Graphs: Notes and Practical Considerations for Researchers, Tech Report HPL-2003-4, HP Laboratories (2003)http://home.comcast.net/~tom.fawcett/public_html/papers/ROC101.pdf
  14. 14.
  15. 15.
  16. 16.
  17. 17.

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Guorong Xuan
    • 1
  • Yun Q. Shi
    • 2
  • Cong Huang
    • 1
  • Dongdong Fu
    • 2
  • Xiuming Zhu
    • 1
  • Peiqi Chai
    • 1
  • Jianjiong Gao
    • 1
  1. 1.Dept. of Computer ScienceTongji UniversityShanghaiP.R. China
  2. 2.Dept. of Electrical & Computer EngineeringNew Jersey Institute of Technology NewarkNew JerseyUSA

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