Advertisement

Informed Detection Revisited

  • Jeffrey A. Bloom
  • Matt L. Miller
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3304)

Abstract

Watermarking systems can employ either informed detection, where the original cover work is required, or blind detection, where it is not required. While early systems used informed detection, recent work has focused on blind detection, because it is considered more challenging and general. Further, recent work on “dirty-paper watermarking” has suggested that informed detection provides no benefits over blind detection.

This paper discusses the dirty-paper assumptions and questions whether they apply to real-world watermarking. We discuss three basic ways in which an informed video-watermark detector, developed at Sarnoff, uses the original work: canceling interference between the cover work and the watermark, canceling subsequent distortions, and tailoring the watermark to the perceptual characteristics of the source. Of these, only the first is addressed by theoretical work on dirty-paper watermarking. Whether the other two can be accomplished equally well with blind watermarking is an open question.

Keywords

Noise Source Channel Capacity Power Constraint Side Information Digital Watermark 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bloom, J.A., Cox, I.J., Kalker, T., Linnartz, J.-P., Miller, M.L., Traw, B.: Copy protection for DVD video. Proc. IEEE 87(7), 1267–1276 (1999)CrossRefGoogle Scholar
  2. 2.
    Chen, B., Wornell, G.W.: An information-theoretic approach to the design of robust digital watermarking systems. IEEE Transactions on Acoustics, Speech, and Signal Processing (1999)Google Scholar
  3. 3.
    Chen, B., Wornell, G.W.: Quantization index modulation: A class of provably good methods for digital watermarking and information embedding. In: Proc. Int. Symp. Inform. Theory, ISIT-2000 (2000)Google Scholar
  4. 4.
    Cheng, H.: Temporal video registration. In: Proc. of IEEE Int’l Conf. on Acoustics, Speech and Signal Processing (ICASSP 2003), Hong Kong, China, April 2003, vol. 3, pp. 489–92 (2003)Google Scholar
  5. 5.
    Cheng, H., Isnardi, M.: Spatial, temporal and histogram video registration for digital watermark detection. In: Proc. of IEEE Int’l Conf. on Image Processing (ICIP 2003), Barcelona, Spain, Septemper 2003, vol. 2, pp. 735–738 (2003)Google Scholar
  6. 6.
    Jim Chou, S., Pradhan, S., Ramchandran, K.: On the duality between distributed source coding and data hiding. Thirty-third Asilomar conference on signals, systems, and computers 2, 1503–1507 (1999)Google Scholar
  7. 7.
    Cohen, A.S., Lapidoth, A.: Generalized writing on dirty paper. In: International Symposium on Information Theory, ISIT (2002)Google Scholar
  8. 8.
    Costa, M.: Writing on dirty paper. IEEE Trans. Inform. Theory 29, 439–441 (1983)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Cox, I.J., Miller, M.L., Bloom, J.A.: Digital Watermarking. Morgan Kaufmann, San Francisco (2001)Google Scholar
  10. 10.
    Daly, S.: The Visible Difference Predictor: An algorithm for the assessment of image fidelity. In: Watson, A.B. (ed.) Digital Images and Human Vision, ch.14, pp. 179–206. MIT Press, Cambridge (1993)Google Scholar
  11. 11.
    Eggers, J.J., Su, J.K., Girod, B.: A blind watermarking scheme based on structured codebooks. In: IEE Seminar on Secure Images and Image Authetication, pp. 4-1–1-21 (2000)Google Scholar
  12. 12.
    Erez, U., Shamai, S., Zamir, R.: Capacity and lattice-strategies for canceling known interferance. In: Proc. of the Cornell Summer Workshop on Inform. Theory (August 2000)Google Scholar
  13. 13.
    Fritz, B., Gray, T.M.: Acad member tied to piracy bust. Variety, January 23 (2004)Google Scholar
  14. 14.
    Secure Digital Music Initiative. SDMI portable device specification (1999), Available at, http://www.sdmi.org
  15. 15.
    Lubin, J.: The use of psychophysical data and models in the analylsis of display system performance. In: Watson, A.B. (ed.) Digital Images and Human Vision, ch.14 pp. 163–178. MIT Press, Cambridge (1993)Google Scholar
  16. 16.
    Lubin, J., Bloom, J.A., Cheng, H.: Robust, content-dependent, high-fidelity watermark for tracking in digital cinema. Security and Watermarking of Multimedia Contents V, SPIE 5020, 536–545 (2003)CrossRefGoogle Scholar
  17. 17.
    Miller, M.L.: Watermarking with dirty-paper codes. In: IEEE International Conference on Image Processing (September 2001)Google Scholar
  18. 18.
    Miller, M.L., Doërr, G.J., Cox, I.J.: Applying informed coding and embedding to design a robust, high capacity watermark. IEEE Transactions on Image Processing 13(6), 792–807 (2004)CrossRefGoogle Scholar
  19. 19.
    Christian, J., van den Branden, L., Farrell, J.E.: Perceptual quality metric for digitally coded color images. In: Proc. EUSIPCO, pp. 1175–1178 (1996)Google Scholar
  20. 20.
    Voyatzis, G., Pitas, I.: The use of watermark in the protection of digital multimedia products. Proceedings of the IEEE, Special Issue on Identication and Protection of Multimedia Information 87(7), 1197–1207 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Jeffrey A. Bloom
    • 1
  • Matt L. Miller
    • 2
  1. 1.Sarnoff CorporationPrincetonUSA
  2. 2.NEC Labs AmericaPrincetonUSA

Personalised recommendations