The wavelet coefficients of images show heavy-tailed marginal statistics as well as strong inter- and intra-subbands and across orientations dependencies. The vector-based hidden Markov model (HMM) has been shown to be an effective statistical model for wavelet coefficients, which is capable of capturing both the subband marginal distribution and the inter-scale and intra-scale dependencies of the wavelet coefficients. In this paper, we propose a locally-optimum watermark detector using the HMM model for image wavelet coefficients. The performance of the proposed detector is studied through simulation and is shown to be superior to that of other detectors in terms of the imperceptibility of the embedded watermark and detection rate.