Please use this identifier to cite or link to this item: https://doi.org/10.1117/12.526642
Title: Wavelet domain watermarking using maximum-likelihood detection
Authors: Ming, N.T. 
Garg, H.K. 
Keywords: Generalized Gaussian
Maximum-likelihood
Neyman-Pearson
Wavelet
Issue Date: 2004
Source: Ming, N.T., Garg, H.K. (2004). Wavelet domain watermarking using maximum-likelihood detection. Proceedings of SPIE - The International Society for Optical Engineering 5306 : 816-826. ScholarBank@NUS Repository. https://doi.org/10.1117/12.526642
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' 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.
Source Title: Proceedings of SPIE - The International Society for Optical Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/72182
ISSN: 0277786X
DOI: 10.1117/12.526642
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