A Maximum Likelihood Watermark Decoding Scheme

Wenming Lu, Wanqing Li, Reihaneh Safavi-Naini, Philip Ogunbona

Abstract: Based on the observation that an attack applied on a watermarked image, from a decoding point of view, modifies the distribution of the detection values away from the ideal distribution (without attack) for corresponding watermarking scheme, we propose a generic maximum likelihood decoding scheme by approximating the distribution with a finite Gaussian mixture model. The parameters of the model are estimated using expectation-maximization algorithm. The scheme allows the decoding to be automatically adapted to attacks that the watermarked images have undergone and, in consequence, to improve the decoding accuracy. Experiments on a QIM based watermarking system have clearly verified the significant improvement of the decoding accuracy achieved by the proposed maximum likelihood decoding in comparison to conventional threshold decoding.