NSCT Domain Additive Watermark Detection Using RAO Hypothesis Test and Cauchy Distribution

Abstract

We presented a RAO hypothesis detector by modeling Cauchy distribution for the Nonsubsampled Contourlet Transform (NSCT) subband coefficients in the field of additive spread spectrum image watermarking. Firstly, the NSCT subband coefficients were modeled following the Cauchy distributions, and the Fit of Goodness shows that Cauchy distribution fits the NSCT subband coefficients more accurately than the Generalized Gaussian Distribution (GGD) commonly used. Secondly, a blind RAO test watermark detector was derived in the NSCT domain, which does not need the knowledge of embedding strength at the receiving end. Finally, compared to the other three state-of-art detectors, the robustness of the proposed watermarking scheme was evaluated when the watermarked images were attacked by JPEG compression, random noise, low pass filtering, and median filtering. Experimental results show that, compared with the other three detectors, the proposed RAO detector guarantees the lower probability of miss under the given probability of false alarm.