Traditionally, digital 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 optimal only when the watermark embedding process follows an additive rule and when the medium is drawn from Gaussian distributions. More recent works on watermark detection are based on decision theory. In this article, a maximum likelihood detection scheme based on Bayes' decision theory is proposed for image watermarking in the 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.