Statistical Robustness in Multiplicative Watermark Detection.

The requirement of robustness is of fundamental importance for all watermarking schemes in various application scenarios. When talking about watermark robustness, we usually mean that the receiver performance degrades smoothly with the attack power. Here we look from another angle, i.e., robustness in statistics. A new detector structure which is robust to small uncertainties in host signal modeling for multiplicative watermarking in the discrete Fourier transform (DFT) domain is presented. By relying on robust statistics theory, an ε-contamination model is applied to describe the magnitudes of the DFT spectrum, based on which we are able to derive a minimax detector that is most robust in a well-defined sense. Experiments on real images demonstrate that the new watermark detector performs more stably than classical ones. [ABSTRACT FROM AUTHOR]