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Computing the Probability of False Watermark Detection

  • Matt L. Miller
  • Jeffrey A. Bloom
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1768)

Abstract

Several methods of watermark detection involve computing a vector from some input media, computing the normalized correlation between that vector and a predefined watermark vector, and comparing the result against a threshold. We show that, if the probability density function of vectors that arise from random, unwatermarked media is a zero-mean, spherical Gaussian, then the probability that such a detector will give a false detection is given exactly by a simple ratio of two definite integrals. This expression depends only on the detection threshold and the dimensionality of the watermark vector.

Keywords

Normalize Correlation Image Watermark False Detection Data Hiding Detection Region 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Matt L. Miller
    • 1
  • Jeffrey A. Bloom
    • 1
  1. 1.Signafy, Inc.Princeton

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