Additive watermark detector in contourlet domain using the t location-scale distribution

One application of digital watermarking is multimedia copyright protection. This paper address the blind watermark detection problem that copyright protection belongs to this field. Since watermark detection process can be modeled as a statistical decision rule, selecting appropriate statistical model plays an important role. This work presents a novel additive watermark detector for contourlet domain image watermarking. Contourlet coefficients of images have non-Gaussian property so, this property should be considered in modeling these coefficients. In this way, we use t location-scale distribution that have non-Gaussian property for modeling contourlet coefficients. Experimental results verify the high efficiency of the proposed detector, its higher performance in comparison with generalized Gaussian and Cauchy detector also its robustness against common image processing attacks such as image compression, median filtering and rotation.