Robust Detection of Transform Domain Additive Watermarks

Deviations of the actual coefficient distributions from the idealized theoretical models due to inherent modeling errors and possible attacks are big challenges for watermark detection. These uncertain deviations may degrade or even upset the performance of existing optimum detectors that are optimized at idealized models. In this paper, we present a new detection structure for transform domain additive watermarks based on Huber’s robust hypothesis testing theory. The statistical behaviors of the image subband coefficients are modeled by a contaminated generalized Gaussian distribution (GGD), which tries to capture small deviations of the actual situation from the idealized GGD. The robust detector is a min-max solution of the contamination model and turns out to be a censored version of the optimum probability ratio test. Experimental results on real images confirm the superiority of the proposed detector with respect to the classical optimum detector.