Abstract
We compare two image bases with respect to their capabilities for image modeling and steganalysis. The first basis consists of wavelets, the second is a Laplacian pyramid. Both bases are used to decompose the image into subbands where the local dependency structure is modeled with a linear Bayesian estimator. Similar to existing approaches, the image model is used to predict coefficient values from their neighborhoods, and the final classification step uses statistical descriptors of the residual. Our findings are counter-intuitive on first sight: Although Laplacian pyramids have better image modeling capabilities than wavelets, steganalysis based on wavelets is much more successful. We present a number of experiments that suggest possible explanations for this result.
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References
Adelson, E.H., Burt, P.J.: Image data compression with the Laplacian pyramid. In: Proceedings of the 1981 Conference on Pattern Recognition and Information Processing, pp. 218–223. IEEE Computer Society Press, Los Alamitos (1981)
Avcibas, I., Memon, N.D., Sankur, B.: Steganalysis using image quality metrics. IEEE Transactions on Image Processing 12(2), 221–229 (2003)
Brodatz, P.: Textures: A Photographic Album for Artists and Designers. Dover Publications, New York (1966)
Buccigrossi, R.W., Simoncelli, E.P.: Image compression via joint statistical characterization in the wavelet domain. IEEE Transactions on Image Processing 8(12), 1688–1701 (1999)
Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines (2001), software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm/
Donoho, D., Johnstone, I., Johnstone, I.M.: Ideal spatial adaptation by wavelet shrinkage. Biometrika 81, 425–455 (1993)
Geisser, S., Eddy, W.F.: A predictive approach to model selection. Journal of the American Statistical Association 74(365), 153–160 (1979)
Goljan, M., Fridrich, J.J., Holotyak, T.: New blind steganalysis and its implications. In: Security, Steganography, and Watermarking of Multimedia Contents VIII 6072(1), pp. 1–13 (February 2006)
Haykin, S.: Neural Networks, 2nd edn. Prentice-Hall, Upper Saddle River (1999)
Holotyak, T., Fridrich, J.J., Soukal, D.: Stochastic approach to secret message length estimation in ±k embedding steganography. In: Delp, E.J., Wong, P.W. (eds.) Security, Steganography, and Watermarking of Multimedia Contents. Proceedings of SPIE, vol. 5681, pp. 673–684. International Society for Optical Engineering, SPIE, San Jose (2005)
Holotyak, T., Fridrich, J.J., Voloshynovskiy, S.: Blind statistical steganalysis of additive steganography using wavelet higher order statistics. In: Dittmann, J., Katzenbeisser, S., Uhl, A. (eds.) CMS 2005. LNCS, vol. 3677, pp. 273–274. Springer, Heidelberg (2005)
Ker, A.D.: Improved detection of LSB steganography in grayscale images. In: Fridrich, J. (ed.) IH 2004. LNCS, vol. 3200, pp. 97–115. Springer, Heidelberg (2004)
Lyu, S., Farid, H.: Steganalysis using higher-order image statistics. IEEE Transactions on Information Forensics and Security 1(1), 111–119 (2006)
Rasmussen, C.E.: Gaussian processes in machine learning. In: Bousquet, O., von Luxburg, U., Rätsch, G. (eds.) Machine Learning 2003. LNCS (LNAI), vol. 3176, pp. 63–71. Springer, Heidelberg (2004)
Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. MIT Press, Cambridge (2006)
Schwamberger, V., Franz, M.O.: Simple algorithmic modifications for improving blind steganalysis performance. In: Proceedings of the 2010 Workshop on Multimedia and Security, MM&Sec 2010. ACM Press, New York (2010)
Schölkopf, B., Smola, A.J.: Learning with Kernels. In: Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press, Cambridge (2002)
Simoncelli, E.P., Adelson, E.H.: Subband transforms. In: Woods, J.W. (ed.) Subband Image Coding. Kluwer Academic Publishers, Norwell (1990)
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Schwamberger, V., Le, P.H.D., Schölkopf, B., Franz, M.O. (2010). The Influence of the Image Basis on Modeling and Steganalysis Performance. In: Böhme, R., Fong, P.W.L., Safavi-Naini, R. (eds) Information Hiding. IH 2010. Lecture Notes in Computer Science, vol 6387. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16435-4_11
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DOI: https://doi.org/10.1007/978-3-642-16435-4_11
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