Weak Signal Watermark Detection Through Rao-T Hypothesis and Lightweight Detection

In this work, we investigate an asymptotically optimal blind zero-bit watermark detector in the wavelet domain. More specifically, assuming that the marginal distribution of detail coefficients is non-Gaussian, we model it with the Student's t probability density function. Furthermore, we assume that the embedding power of the hidden information is unknown, suggesting in this way a new test statistic based on the Rao hypothesis test. The proposed detector exhibits better performance in terms of detection sensitivity and robust properties compared with other known methods in the framework of non-Gaussian environment. Additionally, we investigate a fixed-parameterization approach towards a lightweight detection with regard of time complexity.