A Lightweight Rao-Cauchy Detector for Additive Watermarking in the DWT-Domain

This paper presents a lightweight, asymptotically optimal blind detector for additive spread-spectrum watermark detection in the DWT domain. In our approach, the marginal distributions of the DWT detail subband coefficients are modeled by one-parameter Cauchy distributions and we assume no knowledge of the watermark embedding power. We derive a Rao hypothesis test to detect watermarks of unknown amplitude in Cauchy noise and show that the proposed detector is competitive with the Generalized Gaussian detector, yet is more efficient in terms of required computations.