Locally optimum detection for Barni's multiplicative watermarking in DWT domain

With the increasing demands of copyright protection, watermarking technology has being paid more and more attention. In the design of a watermarking algorithm, a good watermark detection scheme can improve the detection rate, which pushes an increasing number of researchers to work with optimum detectors. In this paper, we propose a locally optimum using a locally most powerful test with respect to Barni's multiplicative water marking which is based on the human visual system (HVS). In the proposed detection scheme, the probability density function of DWT coefficients is modeled using the generalized Gaussian distribution, and the decision threshold is obtained by the Neyman–Pearson (NP) criterion. Additionally, we prove that the existing correlation detection is a special case of the proposed locally optimum detection, and we provide an improved correlation threshold under the condition of locally optimum detection.