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Cryptanalysis of Discrete-Sequence Spread Spectrum Watermarks

  • M. Kivanç Mihçak
  • Ramarathnam Venkatesan
  • Mustafa Kesal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2578)

Abstract

Assume that we are given a watermark (wm) embedding algorithm that performs well against generic benchmark-type attacks that comprise of simple operations that are independent of the algorithm and generally of the input as well. A natural question then is to ask for a nearly perfect cryptanalytic attack for the specific watermarking method. In this paper we present and analyze an attack on a state-of-the-art Discrete-Sequence Spread Spectrum (dsss) audio watermarking algorithm. Our method uses detailed models for watermarked signal and almost always jams or recovers > 90% of the watermarking-key. It exploits the host and wm correlations, and the fact that one can locally correct errors in the wm estimates if the watermarking coefficients are discrete. It is natural to use error-correction codes in a watermarking algorithm, and we study the effects of the accompanying redundancy as well.

Keywords

Image Watermark Watermark Scheme Digital Watermark Watermark Method Watermark Algorithm 
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.

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References

  1. [1]
    I. J. Cox, J. Killian, F. T. Leighton and T. Shamoon, “Secure Spread Spectrum Watermarking for Multimedia,” IEEE Trans. Image Proc., Vol. 6, No. 12, pp. 1673–1687, Dec. 1997. 226Google Scholar
  2. [2]
    F.A.P. Petitcolas and M.G. Kuhn: StirMark software, available from http://www.cl.cam.ac.uk/~fapp2/watermarking/imagewatermarking/stirmark/. 227, 241
  3. [3]
    S. Pereira, S. Voloshynovskiy, M. Madueno, S. Marchand-Maillet and T. Pun: Checkmark software, available from http://watermarking.unige.ch/Checkmark/. 227, 241
  4. [4]
    S. Voloshynovskiy, S. Pereira, T. Pun, J. J. Eggers and J. K. Su, “Attacks on Digital Watermarks: Classification, Estimation-based Attacks and Benchmarks,” IEEE Communications Magazine (Special Issue on Digital watermarking for copyright protection: a communications perspective), F. Bartolini, I. J. Cox, J. Hernandez, F. Pérez-González, Guest Eds., Vol. 39, No. 8, pp. 118–127, 2001, Invited paper. 227Google Scholar
  5. [5]
    G. Marsaglia: Diehard software, available from http://stat.fsu.edu/~geo/diehard.html. 227
  6. [7]
    A. J. Menezes, P.C. van Oorschot and S.A. Vanstone, Handbook of Applied Cryptography, CRC Press, 1997. 227Google Scholar
  7. [8]
    R. Venkatesan and M. H. Jakubowski, “Robust Image Watermarking,” Proc. ICIP, Vancouver, B.C., Canada, 2000. 227, 228, 229, 241Google Scholar
  8. [9]
    R. Venkatesan, “Signal Processing in the Presence of Adversary,” preprint, available from http://research.microsoft.com/~venkie/. 227
  9. [10]
    S. Voloshynovskiy, S. Pereira, V. Iquise and T. Pun, “Attack modeling: Towards a secondgen eration benchmark”, Signal Processing, Special Issue on Information Theoretic Issues in Digital Watermarking, Vol. 81, No. 6, pp. 1177–1214, June 2001. 228zbMATHGoogle Scholar
  10. [11]
    D. Kirovski and H. S. Malvar, “Robust Covert Communication Over a Public Audio Channel Using Spread Spectrum,” Proceedings of Information Hiding Workshop, Pittsburgh, PA, 2001. 228, 238, 239, 240Google Scholar
  11. [12]
    J.K. Su, J. J. Eggers, and B. Girod, “Analysis of Digital Watermarks Subjected to Optimum Linear Filtering and Additive Noise,” Signal Processing, Special Issue on Information Theoretic Issues in Digital Watermarking, Vol. 81, No. 6., pp. 1141–1175, 2001. 228zbMATHGoogle Scholar
  12. [13]
    H. S. Malvar, “A modulatedcom plex lappedt ransform and applications to audio processing,”, Proc. IEEE ICASSP, Phoenix, AZ, March 1999. 228, 238Google Scholar
  13. [14]
    M.K. Mihcak, R. Venkatesan and M. Kesal, “Discrete-Sequence Spread Spectrum Watermarking Methods and Estimation Attacks,” preprint, August, 2001. 228Google Scholar
  14. [15]
    M.K. Mihcak and R. Venkatesan, “Blind Image Watermarking via Derivation and Quantization of Robust Semi-Global Statistics,” Proc. IEEE ICASSP, Florida, FL, June 2002. 229Google Scholar
  15. [16]
    S. LoPresto, K. Ramchandran and M.T. Orchard, “Image Coding based on Mixture Modeling ofWavelet Coefficients and a Fast Estimation-Quantization Framework,” Proc. Data Compression Conference 1997, Snowbird, Utah, pp. 221–230, 1997. 230Google Scholar
  16. [17]
    M.K. Mihcak, I. Kozintsev, K. Ramchandran and P. Moulin, “Low-Complexity Image Denoising Basedon Statistical Modeling of Wavelet Coefficients,” IEEE Signal Processing Letters, Vol. 6, No. 12, pp. 300–303, Dec. 1999. 230CrossRefGoogle Scholar
  17. [18]
    H.L. Van Trees, Detection, Estimation and Modulation Theory, Wiley, 1968. 231Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • M. Kivanç Mihçak
    • 1
  • Ramarathnam Venkatesan
    • 1
  • Mustafa Kesal
    • 2
  1. 1.Microsoft ResearchUSA
  2. 2.University of IllinoisUrbana-Champaign

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