Infonnation-theoretic analysis of spherical fingerprinting

Pierre Moulin, Ying Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Information-theoretic performance limits of digital fingerprinting systems subject to almost-sure squared-error distortion constraints on the fingerprint embedder and the colluders are derived in this paper. The rate of the fingerprinting code is R= 1/N log M where is codelength and is the number of users. No assumption is made on the host signal statistics, but the collusion channel is also subject to a location-invariant condition. The receiver knows neither the collusion channel nor even the number of colluders. Capacity is the supremum of achievable rates and is shown to be equal to 1/2K log( 1+D f/KDc) where is the number of colluders, and Df and Dc are the L2-distortion tolerance levels for the fingerprint embedder and the colluders, respectively. The worst collusion is shown to consist of uniform linear averaging of the coalition's marked copies followed by addition of independent spherical noise. Positive error exponents are achieved at all rates below capacity using random spherical fingerprinting codes and a new universal decoding criterion based on empirical Gaussian mutual information. It is also shown that minimum-distance decoding fails for this problem, and that a simple single-user decoder is almost as good as the universal decoder for large Geometric interpretations for all the results are given.

Original languageEnglish (US)
Title of host publicationInformation Theory and Applications Workshop, ITA 2009
Pages229-236
Number of pages8
DOIs
StatePublished - 2009
EventInformation Theory and Applications Workshop, ITA 2009 - San Diego, CA, United States
Duration: Feb 8 2009Feb 13 2009

Publication series

NameInformation Theory and Applications Workshop, ITA 2009

Other

OtherInformation Theory and Applications Workshop, ITA 2009
CountryUnited States
CitySan Diego, CA
Period2/8/092/13/09

Keywords

  • Capacity
  • Digital fingerprinting
  • Error exponents
  • Gaussian random variables
  • Model order selection
  • Multiple-access channels
  • Normalized correlation
  • Randomized codes
  • Typical sets
  • Universal coding

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems

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