Statistically Robust Detection of Multiplicative Spread-Spectrum Watermarks

The uncertainties in host signal modeling due to inherent model errors and various attack distortions have prompted the introduction of robust statistics theory in the context of watermark detection. Specifically, the $\epsilon$-contamination model was applied to describe the host signals, and statistically robust (SR) watermark detectors assuming known embedding strengths were derived. In this work, we investigate the robust detection structure for multiplicative watermarking. A detection-simulation (DS)-based approach to determine the contamination factor is also presented. Moreover, considering that the strengths of the watermark signals may be adapted to host signals and will very likely change after being distorted by attacks, we go further to propose the asymptotically robust detector for multiplicative watermarks, which can be viewed as the SR counterpart of the locally most powerful watermark detector in the same sense that the SR detector with full knowledge of the watermark strengths is the corresponding parallel for the optimum detector. Experiments on real images demonstrate the superiority of the new schemes over the conventional ones.