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).
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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)
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-JoancomartÃ, J., Katzenbeisser, S., Pérez-González, F. (eds.) IH 2005. LNCS, vol. 3727, pp. 262–277. Springer, Heidelberg (2005)
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)
Provos, N.: Defending against statistical steganalysis. In: 10th USENIX Security Symposium, Washington DC, USA (2001)
Westfeld, A.: F5-A steganographic algorithm. In: Moskowitz, I.S. (ed.) IH 2001. LNCS, vol. 2137, p. 289. Springer, Heidelberg (2001)
Sallee, P.: Model-based methods for steganography and steganalysis. International Journal of Image and Graphics 5(1), 167–190 (2005)
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)
Haralick, R.M.: Textural features for image classification. IEEE Trans. Systems Man Cybernetics SMC-3 (1973)
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)
Shi, Y.Q., Sun, H.: Image and Video Compression for Multimedia Engineering: Fundamentals, Algorithms, and Standards. CRC Press, Boca Raton (1999)
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
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Xuan, G. et al. (2006). Steganalysis Using High-Dimensional Features Derived from Co-occurrence Matrix and Class-Wise Non-Principal Components Analysis (CNPCA). In: Shi, Y.Q., Jeon, B. (eds) Digital Watermarking. IWDW 2006. Lecture Notes in Computer Science, vol 4283. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11922841_5
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DOI: https://doi.org/10.1007/11922841_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-48825-5
Online ISBN: 978-3-540-48827-9
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