Digital Watermarking Enhancement Using Wavelet Filter Parametrization

  • Piotr Lipiński
  • Jan Stolarek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6593)


In this paper a genetic-based enhancement of digital image watermarking in the Discrete Wavelet Transform domain is presented. The proposed method is based on adaptive synthesis of a mother wavelet used for image decomposition. Wavelet synthesis is performed using parametrization based on an orthogonal lattice structure. A genetic algorithm is applied as an optimization method to synthesize a wavelet that provides the best watermarking quality in respect to the given optimality criteria. Effectiveness of the proposed method is demonstrated by comparing watermarking results using synthesized wavelets and the most commonly used Daubechies wavelets. Experiments demonstrate that mother wavelet selection is an important part of a watermark embedding process and can influence watermarking robustness, separability and fidelity.


watermarking adaptive wavelets genetic algorithms 


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  1. 1.
    Buse, R.D., Mwangi, E.: A digital image watermarking scheme based on localised wavelet coefficients dependency. In: AFRICON 2009, pp. 1–4 (September 2009)Google Scholar
  2. 2.
    Chang, C.-C., Tai, W.-L., Lin, C.-C.: A multipurpose wavelet-based image watermarking. In: First International Conference on Innovative Computing, Information and Control, ICICIC 2006, vol. 3, pp. 70–73 (2006)Google Scholar
  3. 3.
    Cheng, G., Yang, J.: A watermark scheme based on two-dimensional wavelet filter parametrization. In: Fifth International Conference on Informaion Assurance and Security, pp. 301–304 (2009)Google Scholar
  4. 4.
    Cooklev, T.: An efficient architecture for orthogonal wavelet transforms. IEEE Signal Processing Letters 13(2) (February 2006)Google Scholar
  5. 5.
    Cox, I.J., Miller, M.L., Bloom, J.A., Fridrich, J., Kalker, T.: Digital Watermarking and Steganography, 1st edn. Elsevier, Amsterdam (2008)Google Scholar
  6. 6.
    Dietl, W., Meerwald, P., Uhl, A.: Protection of wavelet-based watermarking systems using filter parametrization. Signal Processing 83(10), 2095–2116 (2003)CrossRefzbMATHGoogle Scholar
  7. 7.
    Dietze, M., Jassim, S.: Filter ranking for DWT-domain robust digital watermarking. EURASIP Journal on Applied Signal Processing 14, 2093–2101 (2004)CrossRefGoogle Scholar
  8. 8.
    Ejima, M., Miyazaki, A.: A wavelet-based watermarking for digital images and video. In: Proceedings of International Conference on Image Processing, 2000, vol. 3, pp. 678–681 (2000)Google Scholar
  9. 9.
    Huang, Z.Q., Jiang, Z.: Watermarking still images using parametrized wavelet systems. In: Image and Vision Computing, Institute of Information Sciences and Technology, Massey University (2003)Google Scholar
  10. 10.
    Huo, F., Gao, X.: A wavelet based image watermarking scheme. In: IEEE International Conference on Image Processing 2006, pp. 2573–2576 (October 2006)Google Scholar
  11. 11.
    Kim, J.R., Moon, Y.S.: A robust wavelet–based digital watermark using level-adaptive thresholding. In: Proceedings of the 6th IEEE International Conference on Image Processing, ICIP 1999 (October 1999)Google Scholar
  12. 12.
    Kowalczuk, Z., Białaszewski, T.: Genetic algorithms in multi–objective optimization of detection observers. In: Korbicz, J., Kościelny, J.M., Kowalczuk, Z., Cholewa, W. (eds.) Fault Diagnosis. Models, Artificial, Intelligence, Applications, pp. 511–556. Springer, Heidelberg (2004)Google Scholar
  13. 13.
    Miyazaki, A.: A study on the best wavelet filter bank problem in the wavelet-based image watermarking. In: 18th European Conference on Circuit Theory and Design, ECCTD 2007, pp. 184–187 (August 2007)Google Scholar
  14. 14.
    Rieder, P., Gotze, J., Nossek, J.S., Burrus, C.S.: Parameterization of orthogonal wavelet transforms and their implementation. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 45(2), 217–226 (1998)CrossRefGoogle Scholar
  15. 15.
    Stasiak, B., Yatsymirskyy, M.: Fast orthogonal neural networks. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 142–149. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  16. 16.
    Stolarek, J.: Improving energy compaction of a wavelet transform using genetic algorithm and fast neural network. Archives of Control Sciences 20(4), 381–397 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Stolarek, J., Lipiński, P.: Improving digital watermarking fidelity using fast neural network for adaptive wavelet synthesis. Journal of Applied Computer Science 18(1), 61–74 (2010)Google Scholar
  18. 18.
    Stolarek, J., Yatsymirskyy, M.: Fast neural network for synthesis and implementation of orthogonal wavelet transform. In: Image Processing & Communications Challenges. AOW EXIT (2009)Google Scholar
  19. 19.
    Tsai, M.-J., Hung, H.-Y.: DCT and DWT–based image watermarking by using subsampling. In: Proceedings of 24th International Conference on Distributed Computing Systems Workshops, 2004, pp. 184–189 (2004)Google Scholar
  20. 20.
    Wang, Y., Doherty, J.F., Van Dyck, R.E.: A wavelet-based watermarking algorithm for ownership verification of digital images. IEEE Transactions on Image Processing 11(2), 77–88 (2002)CrossRefGoogle Scholar
  21. 21.
    Yatsymirskyy, M.: Lattice structures for synthesis and implementation of wavelet transforms. Journal of Applied Computer Science 17(1), 133–141 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Piotr Lipiński
    • 1
  • Jan Stolarek
    • 1
  1. 1.Institute of Information TechnologyTechnical University of LodzPoland

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