Mr. Tek Ming Ng Profile

Tek Ming Ng

at National Univ of Singapore

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Proceedings Article | 22 June 2004 Paper

Wavelet domain watermarking using maximum-likelihood detection

Tek Ng, Hari Garg

Proceedings Volume 5306, (2004) https://doi.org/10.1117/12.526642

KEYWORDS: Digital watermarking, Discrete wavelet transforms, Probability theory, Wavelets, Multimedia, Image quality, Sensors, Field emission displays, Image compression, Detection and tracking algorithms

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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.

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