Abstract
Watermarking security has emerged as the domain of extensive research in recent years. This paper presents both information theoretic analysis and practical attack algorithm for spread-spectrum based watermarking security incorporating natural scene statistics (NSS) model. Firstly, the Gaussian scale mixture (GSM) is introduced as the NSS model. The security is quantified by the mutual information between the observed watermarked signals and the secret carriers. The new security measures are then derived based on the GSM model, which allows for more accurate evaluation of watermarking security. Finally, the practical attack algorithm is developed in the framework of variational Bayesian ICA, which is shown to increase the performance and flexibility by allowing incorporation of prior knowledge of host signal. Extensive simulations are carried out to demonstrate the feasibility and effectiveness of the proposed algorithm.
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References
Cox, I.J., Miller, M.L., Bloom, J.A.: Digital Watermarking. Elsevier Science, Amsterdam (2002)
Kalker, T.: Consideration on watermarking security. In: Proc. MMSP, Cannes, France, pp. 201–206 (October 2001)
Furon, T., et al.: Security Analysis, European Project IST-1999-10987 CERTIMARK, Deliverable D.5.5 (2002)
Kerckhoffs, A.: La cryptographie militaire. Journal des militaries 9, 5–38 (1983)
Pérez-Freire, L., Comesaña, P., Troncoso-Pastoriza, J.R., Pérez-González, F.: Watermarking security: a survey. In: Shi, Y.Q. (ed.) Transactions on Data Hiding and Multimedia Security I. LNCS, vol. 4300, pp. 41–72. Springer, Heidelberg (2006)
Cayre, F., Fontaine, C., Furon, T.: Watermarking security: Theory and practice. IEEE Trans. Signal Processing 53, 3976–3987 (2005)
Comesaña, P., Pérez-Freire, L., Pérez-Gonzále, F.: Fundamentals of data hiding security and their application to spread-spectrum analysis. In: Barni, M., Herrera-JoancomartÃ, J., Katzenbeisser, S., Pérez-González, F. (eds.) IH 2005. LNCS, vol. 3727, pp. 146–160. Springer, Heidelberg (2005)
Lyu, S., Simoncelli, E.P.: Statistical modeling of images with fields of Gaussian scale mixtures. Advances in Neural Information Processing Systems 19 (May 2007)
Wainwright, M.J., Simoncelli, E.P., Wilsky, A.S.: Random cascades on wavelet trees and their use in analyzing and modeling natural images. Appl. Comput. Harmon. Anal. 11, 89–123 (2001)
Sheikh, H.R., Bovik, A.C., Veciana, C.: An information fidelity criterion for image quality assessment using natural scene statistics. IEEE Trans. Image Processing 14(12), 2117–2128 (2005)
Cover, T.M., Thomas, J.A.: Elements of Information Theory. John Wiley & Sons, Chichester (1991)
Hyvarinen, A., Oja, E.: Independent component analysis: Algorithms and Applications. Neural Networks 13, 411–430 (2000)
Hyvarinen, A.: Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Networks 10(3), 626–634 (1999)
Choudrey, R.A.: Variational Method for Bayesian Independent Component Analysis, Ph.D. Thesis, Oxford University (2002)
Choudrey, R.A., Robert, S.J.: Variational mixture Bayesian independent component analysis. Neural Computation 15(1) (2003)
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Ni, J., Zhang, R., Fang, C., Huang, J., Wang, C., Kim, HJ. (2008). Watermarking Security Incorporating Natural Scene Statistics. In: Solanki, K., Sullivan, K., Madhow, U. (eds) Information Hiding. IH 2008. Lecture Notes in Computer Science, vol 5284. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88961-8_10
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DOI: https://doi.org/10.1007/978-3-540-88961-8_10
Publisher Name: Springer, Berlin, Heidelberg
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