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Fidelity-Controlled Robustness Enhancement of Blind Watermarking Schemes Using Evolutionary Computational Techniques

  • Chun-Hsiang Huang
  • Chih-Hao Shen
  • Ja-Ling Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3304)

Abstract

Designing optimal watermarking schemes is inherently an interesting and difficult problem since the three most important performance requirements of digital watermarking – fidelity, robustness and watermark capacity – are conflicting with each other. Nowadays, most watermarking schemes hide the watermark information in a heuristic manner, that is, watermarks are often embedded according to predefined rules and empirical parameter settings. Therefore, the performance of digital watermarking can only be passively decided and evaluated, rather than being actively adopted as additional clues helpful to achieve better performance in embedding modules. In this paper, watermark embedding is simulated as an optimization procedure in which optimal embedded results are obtained by using important evolutionary computation techniques – the genetic algorithms. Under the condition that fixed amount of watermark bits are hidden, in this work, the minimal fidelity requirement of embedded content can be specified by users in advance and guaranteed throughout the embedding procedure. Furthermore, concrete measures of the robustness against certain attacks are treated as the objective functions that guide the optimizing procedure. In other words, a blind watermarking scheme with application-specific data capacity, guaranteed fidelity, and theoretically optimal robustness against certain types of attacks is proposed. Experimental results clearly show that the proposed scheme possesses great performance improvements over the original one. More importantly, the proposed enhancing approach has many desired architectural characteristics, such as blind detection, asymmetric embedding/detection overheads, as well as embedding and detection in different domains.

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References

  1. 1.
    Cox, I.J., Bloom, J., Miller, M.L.: Digital Watermarking, 1st edn. Morgan Kaufmann Publishers, San Francisco (2001)Google Scholar
  2. 2.
    Cox, I.J., Miller, M.L.: The First 50 Years of Electronic Watermarking. Journal of Applied Signal Processing 2, 126–132 (2002)CrossRefzbMATHGoogle Scholar
  3. 3.
    Fogel, D.B.: Evolutionary Computation toward a New Philosophy of Machine Intelligence. IEEE Press, Los Alamitos (1995)zbMATHGoogle Scholar
  4. 4.
    Goldberg, D.E.: Genetic Algorithm in Search, Optimization & Machine Learning. Addison Wesley, Reading (1989)zbMATHGoogle Scholar
  5. 5.
    Huang, C.H., Wu, J.L.: A Watermark Optimization Technique Based on Genetic Algorithms. In: SPIE Electronic Imaging 2000, San Jose (January 2000)Google Scholar
  6. 6.
    Hsu, C.T., Wu, J.L.: Hidden Digital Watermarks in Images. IEEE Transactions on Image Processing 8(1) (January 1999)Google Scholar
  7. 7.
    Wu, J.L., Lin, C.H., Huang, C.H.: An Efficient Genetic Algorithm for Small Range Search Problem. In: Intelligent Multimedia Processing with Soft Computing, pp. 253–280. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  8. 8.
    Pan, J.S., Huang, H.-C., Wang, F.H.: Genetic Watermarking Techniques. In: The 5th Int’l Conference on Knowledge-based Intelligent Information Engineering System & Allied TechnologiesGoogle Scholar
  9. 9.
    Huang, C.H., Wu, J.L.: A Blind Watermarking Algorithm with Semantic Meaningful Watermarks. In: 34th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove (October 2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Chun-Hsiang Huang
    • 1
  • Chih-Hao Shen
    • 1
  • Ja-Ling Wu
    • 1
  1. 1.Communication and Multimedia Laboratory, Department of Computer Science and Information EngineeringNational Taiwan UniversityTaipeiTaiwan, R. O. C.

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