Sun, ZhongweiFeng, DengguoXue, RuiN. Correia and J. Jorge and T. Chambel and Z. Pan2014-01-262014-01-2620043-905673-17-71812-7118https://doi.org/10.2312/EGMM/MM04/173-179Watermark detection plays a crucial role in digital watermarking. It has traditionally been tackled using correla-tion-based techniques. However, the correlation-based detection is not the optimum choice when the host media doesn t follow a gaussian distribution or the watermark is not embedded in the host media in an additive way. A discrete wavelet transform (DWT) domain multiplicative watermark detection algorithm for digital images is propo-sed in this paper, which exploits the imperceptibility constraint of watermarking. By formulating the watermark detection as weak signal detection in non-gaussian noise, the proposed algorithm is derived according to statistical inference theory. With the wavelet coefficients modeled by generalized gaussian distribution (GGD), the optimum decision threshold for the detector is obtained by applying Neyman-pearson criteria. The superiority of the novel detector in performance is confirmed through Monte Carlo simulations. Keywords: Digital watermarking, Multiplicative embedding, Discrete wavelet transform, Generalized gaussian dis-tribution, Weak signal detection.