Abstract
A new informed image watermarking algorithm is presented in this paper, which can achieve the information rate of 1/64 bits/pixel with high robustness. Firstly, the LOT (Locally Optimum Test) detector based on HMM in wavelet domain is developed to tackle the issue that the exact strength for informed embedding is unknown to the receiver. Then based on the LOT detector, the dirty-paper code for informed coding is constructed and the metric for the robustness is defined accordingly. Unlike the previous approaches of informed watermarking which take the informed coding and embedding process separately, the proposed algorithm implements a jointly coding and embedding optimization for high capacity and robust watermarking. The Genetic Algorithm (GA) is employed to optimize the robustness and distortion constraints simultaneously. Extensive simulations are carried out which demonstrates that the proposed algorithm achieves significant improvements in performance against JPEG, gain attack, low-pass filtering and so on.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cox, I.J., Miller, M.L., McKellips, A.L.: Watermarking as communications with side information. Proc. IEEE 87(7), 1127–1141 (1999)
Miller, M.L., Doerr, G.J., Cox, I.J.: Applying informed coding and embedding to design a robust, high capacity, watermark. IEEE Trans. Image Process 13(6), 792–807 (2004)
Abrardo, A., Barni, M.: Informed watermarking by means of orthogonal and quasi-orthogonal dirty paper coding. IEEE Trans. on Signal Processing 53(2), 824–833 (2005)
Moulin, P., O’Sullivan, J.A.: Information-theoretic analysis of information hiding. In: Proc. IEEE Int. Symp. Inf. Theory, Sorrento, Italy, p. 19 (June 2000)
Moulin, P.: The role of information theory in watermarking and its application to image watermarking. Signal Process 81(6), 1121–1139 (2001)
Cohen, A.S., Lapidoth, A.: The Gaussian watermarking game. IEEE Trans. Inf. Theory 48(6), 1639–1667 (2002)
Yu, W., Sutivong, A., Julian, D., Cover, T.M., Chiang, M.: Writing on colored paper. In: Proc. ISIT, June 24-29, 2001, Washington, DC (2001)
Costa, M.H.M.: Writing on dirty paper. IEEE Trans. Inf. Theory IT 29(3), 439–441 (1983)
Ni, J., Zhang, R., Huang, J., Wang, C.: HMM-based in wavelet domain robust multibit image watermarking algorithm. In: Barni, M., Cox, I., Kalker, T., Kim, H.J. (eds.) IWDW 2005. LNCS, vol. 3710, pp. 110–123. Springer, Heidelberg (2005)
Poor, H.V.: An introduction to signal detection and estimation. Springer, Heidelberg (1994)
Crouse, M.S., Nowak, R.D., Baraniuk, R.G.: Wavelet-Based Statistical Signal Processing Using Hidden Markov Models. IEEE Trans. on Signal Processing 46(4) (April 1998)
Mitsuo, G., Cheng, R.W.: Genetic algorithms and Engineering design. John Wiley & Sons Inc, New York (1997)
Watson, B.: DCT quantization matrices optimized for individual images. Human Vision, Visual Processing, and Digital Display IV SPIE-1913, 202–216 (1993)
Ni, J., Wang, C., Huang, J.: Performance enhancement for DWT-HMM image watermarking with content-adaptive approach. In: Proc. of ICIP 2006, pp. 1377–1380 (2006)
Chen, J., et al.: Scalar quantization noise analysis and optimal bit allocation for wavelet pyramid image coding. IEICE Trans. Fundamental E76-A, 1502–1504 (1993)
Petitcolas, F.A.P.: Watermarking schemes evaluation. IEEE Trans. on Signal Processing 17(5), 58–64 (2000)
StirMark. http://www.cl.cam.ac.uk/~fapp2/watermarking/stirmark/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, C., Ni, J., Huang, J., Zhang, R., Huang, M. (2007). Robust and High Capacity Image Watermarking Based on Jointly Coding and Embedding Optimization. In: Furon, T., Cayre, F., Doërr, G., Bas, P. (eds) Information Hiding. IH 2007. Lecture Notes in Computer Science, vol 4567. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77370-2_5
Download citation
DOI: https://doi.org/10.1007/978-3-540-77370-2_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77369-6
Online ISBN: 978-3-540-77370-2
eBook Packages: Computer ScienceComputer Science (R0)