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Spread-Spectrum Watermark by Synthesizing Texture

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Advances in Multimedia Information Processing – PCM 2007 (PCM 2007)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4810))

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Abstract

Image watermarking is a mapping from watermark message to a set of image counterparts, where every version conveys the same meaning with the original image. Since textures that present single perceptual meaning have certain diversity, an intuitive idea of watermarking is to replace the texture region of an image with a similar-looking synthetic texture containing the watermark. We propose a spread-spectrum watermarking scheme by integrating existent work on texture extraction, segmentation and synthesis, and obtain suggestive results, including (1) the synthetic watermarks can resist adaptive Wiener filtering attack due to its power spectrum similar with the original image; (2) if using the spread-spectrum carrier which is designed elaborately according to the subspace spanned by the textures, hiding capacity can be improved by 20%, while effective hiding capacity under Wiener filtering attack can be doubled; (3) the proposed watermarking scheme also enlighten a sophisticate strategy for watermark attack.

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References

  1. Moulin, P., Koetter, R.: Data-hiding codes. In: Proc. of the IEEE, vol. 93, pp. 2083–2126 (December 2005)

    Google Scholar 

  2. Cox, I.J., Kilian, J., Leighton, F.T., Shamoon, T.: Secure spread spectrum watermarking for multimedia. IEEE Trans. Image Proc. 6, 1673–1687 (1997)

    Article  Google Scholar 

  3. Wei, L.Y., Levoy, M.: Fast texture synthesis using tree-structured vector quantization. In: Proc. ACM SIGGRAPH, pp. 479–488 (2000)

    Google Scholar 

  4. Bender, W., Gruhl, D., Morimoto, N., Lu, A.: Techniques for data hiding. IBM Syst. J. 35(3/4), 313–336 (1996)

    Article  Google Scholar 

  5. Heeger, D., Bergen, J.: Pyramid-based texture analysis/synthesis. In: Proc. ACM SIGGRAPH, pp. 229–238 (1995)

    Google Scholar 

  6. Zhu, S.C., Wu, Y.N., Mumford, D.B.: Filter, Random fields, and Maximum Entropy (FRAME) -Towards a Unified Theory for Texture Modeling. Int’l Journal of Computer Vision 27, 107–126 (1998)

    Article  Google Scholar 

  7. Portilla, J., Simoncelli, E.P.: A parametric texture model based on joint statistics of complex wavelet coefficients. Int’l Journal of Computer Vision 40, 49–71 (2000)

    Article  MATH  Google Scholar 

  8. Portilla, J., Simoncelli, E.: Texture Synthesis (April 2001), http://www.cns.nyu.edu/~lcv/texture/

  9. Balakrishnan, N., Hariharakrishnan, K., Schonfeld, D.: A new image representation algorithm inspired by image submodality models, redundancy reduction, and learning in biological vision. IEEE Trans. Pattern Analysis & Machine Intelligence 27, 1367–1378 (2005)

    Article  Google Scholar 

  10. Tipping, M., Bishop, C.: Mixtures of Probabilistic Principal Component Analyzers. Neural Computation 11(2), 443–482 (1999)

    Article  Google Scholar 

  11. Nabney, I., Bishop, C.: Netlab Neural Network Software (2003), http://www.ncrg.aston.ac.uk/netlab

  12. Hyrarinen, A.: Fast ICA Matlab package (April 2003), http://www.cis.hut.fi/projects/ica

  13. Voloshynovskiy, S., Pereira, S., Pun, T., et al.: Attacks on Digital Watermarks: Classification, Estimation-Based Attacks, and Benchmarks. IEEE Communications Magazine 39, 118–126 (2001)

    Article  Google Scholar 

  14. Zhang, F., Liu, W.Y., Liu, C.X.: High capacity watermarking in nonedge texture under statistical distortion constraint. In: Asian Conf on Computer Vision (to be published)

    Google Scholar 

  15. Simoncelli, E.P., Freeman, W.T.: The Steerable Pyramid: A Flexible Architecture for Multi-Scale Derivative Computation. In: IEEE Int’l Conf on Image Processing, pp. 444–447 (October 1995)

    Google Scholar 

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Horace H.-S. Ip Oscar C. Au Howard Leung Ming-Ting Sun Wei-Ying Ma Shi-Min Hu

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© 2007 Springer-Verlag Berlin Heidelberg

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Liu, W., Zhang, F., Liu, C. (2007). Spread-Spectrum Watermark by Synthesizing Texture. In: Ip, H.HS., Au, O.C., Leung, H., Sun, MT., Ma, WY., Hu, SM. (eds) Advances in Multimedia Information Processing – PCM 2007. PCM 2007. Lecture Notes in Computer Science, vol 4810. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77255-2_37

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  • DOI: https://doi.org/10.1007/978-3-540-77255-2_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-77254-5

  • Online ISBN: 978-3-540-77255-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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