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Robust Blind Image Watermarking with Independent Component Analysis: A Embedding Algorithm

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Computational Intelligence and Bioinspired Systems (IWANN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3512))

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Abstract

The authors propose a new solution to the blind robust watermarking of digital images. In this approach we embed the watermark into the independent components of the image. Since independent components are related to the edges of the image, this method has a little perceptual impact on the watermarked image. Besides, we exploit the orthogonality of independent components and spread-spectrum generated watermarks in the blind extraction of the watermark. As extraction algorithm we use a simple matched filter. We also improve this novel method with standard techniques such as perceptual masking and holographic properties. Some experiments are included to illustrate the good performance of the algorithm against compression, cropping, filtering or quantization based attacks.

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References

  1. Cox, I., Kilian, J., Leighton, T., Shamoon, T.: Secure spread spectrum watermarking for multimedia. IEEE Transactions on Image Processing 6, 1673–1687 (1997)

    Article  Google Scholar 

  2. Noel, S.E., Szu, H.H.: Multimedia authenticity with ICA watermarks. In: Szu, H.H., Vetterli, M., Campbell, W.J., Buss, J.R. (eds.) Proc. SPIE, Wavelet Applications VII, vol. 4056, pp. 175–184 (2000)

    Google Scholar 

  3. Yu, D., Sattar, F.: A new blind watermarking technique based on independent component analysis. In: Petitcolas, F.A.P., Kim, H.-J. (eds.) IWDW 2002. LNCS, vol. 2613, pp. 51–63. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  4. Liu, J., Zhang, X., Sun, J., Lagunas, M.A.: A digital watermarking scheme based on ICA detection. In: Proc. ICA 2003, Nara, Japan, pp. 215–220 (2003)

    Google Scholar 

  5. Murillo-Fuentes, J., Molina-Bulla, H., González-Serrano, F.: Independent component analysis applied to digital image watermarking. In: Proc. ICASSP 2001, Salt Lake City, USA, vol. III, pp. 1997–2000 (2001)

    Google Scholar 

  6. Bounkong, S., Toch, B., Saad, D., Lowe, D.: Ica for watermarking digital images. J. Mach. Learn. Res. 4, 1471–1498 (2003)

    Article  Google Scholar 

  7. Murillo-Fuentes, J.J.: Independent component analysis in the watermarking of digital images. In: Puntonet, C.G., Prieto, A.G. (eds.) ICA 2004. LNCS, vol. 3195, pp. 938–945. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  8. Hyvärinen, A., Karhunen, J., Oja, E.: Independent component analysis. John Willey and Sons, West Sussex (2001)

    Book  Google Scholar 

  9. Murillo-Fuentes, J.J., González-Serrano, F.J.: A sinusoidal contrast function for the blind separation of statistically independent sources. IEEE Trans. on Signal Processing 52, 3459–3463 (2004)

    Article  Google Scholar 

  10. Bell, A.J., Sejnowski, T.J.: Edges are the independent components of natural scenes. In: Mozer, M.C., Jordan, M.I., Petsche, T. (eds.) Advances in Neural Information Processing Systems, vol. 9, p. 831. The MIT Press, Cambridge (1997)

    Google Scholar 

  11. Lee, T., Lewicki, M., Sejnowski, T.: Unsupervised classification with non-gaussian mixture models using ICA. In: Advances in Neural Information Processing Systems, vol. 11, pp. 58–64. The MIT Press, Cambridge (1999)

    Google Scholar 

  12. Hornillo-Mellado, S., Martín-Clemente, R., Acha, J.I., Puntonet, C.G.: Application of independent component analysis to edge detection and watermarking. In: Proc. IWANN 2003, Mahón, Spain, pp. 270–280 (2003)

    Google Scholar 

  13. Bugallo, M.F., Dapena, A., Castedo, L.: Image compression via independent component analysis. In: Learning, Leganés (2000)

    Google Scholar 

  14. Kerckhopffs, A.: La cryptographie militaire. Journal des Sciences Militaires IX, 5-38 (January), 161-191(February) (1883)

    Google Scholar 

  15. Mora-Jimenez, I., Navia-Vazquez, A.: A new spread spectrum watermarking method with self-synchronization capabilities. In: Proc. ICIP 2000, Vancouver, BC, Canada (2000)

    Google Scholar 

  16. Wolfgang, R., Podilchuk, C., Delp, E.J.: Perceptual watermarks for digital images and video. Proceedings of the IEEE 87, 1108–1126 (1999)

    Article  Google Scholar 

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Murillo-Fuentes, J.J., Boloix-Tortosa, R. (2005). Robust Blind Image Watermarking with Independent Component Analysis: A Embedding Algorithm. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_135

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  • DOI: https://doi.org/10.1007/11494669_135

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26208-4

  • Online ISBN: 978-3-540-32106-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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