Wavelet domain watermarking using maximum-likelihood detection

Tek Ming Ng; Hari Krishna Garg

doi:10.1117/12.526642

22 June 2004 Wavelet domain watermarking using maximum-likelihood detection

Tek Ming Ng, Hari Krishna Garg

Proceedings Volume 5306, Security, Steganography, and Watermarking of Multimedia Contents VI; (2004) https://doi.org/10.1117/12.526642
Event: Electronic Imaging 2004, 2004, San Jose, California, United States

Abstract

A digital watermark is an imperceptible mark placed on multimedia content for a variety of applications including copyright protection, fingerprinting, broadcast monitoring, etc. Traditionally, watermark detection algorithms are based on the correlation between the watermark and the media the watermark is embedded in. Although simple to use, correlation detection is only optimal when the watermark embedding process follows an additive rule and when the media is drawn from Gaussian distributions. More recent works on watermark detection are based on decision theory. In this paper, a maximum-likelihood (ML) detection scheme based on Bayes's decision theory is proposed for image watermarking in wavelet transform domain. The decision threshold is derived using the Neyman-Pearson criterion to minimize the missed detection probability subject to a given false alarm probability. The detection performance depends on choosing a probability distribution function (PDF) that can accurately model the distribution of the wavelet transform coefficients. The generalized Gaussian PDF is adopted here. Previously, the Gaussian PDF, which is a special case, has been considered for such detection scheme. Using extensive experimentation, the generalized Gaussian PDF is shown to be a better model.

Citation Download Citation

Tek Ming Ng and Hari Krishna Garg "Wavelet domain watermarking using maximum-likelihood detection", Proc. SPIE 5306, Security, Steganography, and Watermarking of Multimedia Contents VI, (22 June 2004); https://doi.org/10.1117/12.526642

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