Statistical detector for wavelet-based image watermarking using modified GH PDF

A new detector using modified Gauss-Hermite (GH) probability density function (PDF) is proposed for the wavelet-domain image watermarking scheme. It is shown that the proposed PDF matches the empirical one of image wavelet coefficients better than other conventional PDFs such as the generalized Gaussian and Bessel K-form. This is because of the fact that the modified GH PDF utilizes an arbitrary number of higher order moments of the wavelet coefficients instead of considering only the first few for the parameter estimation process. The proposed PDF is then used for designing the statistical detector for a wavelet-based image watermarking algorithm. Experimental results on a standard image database show that the proposed detector provides a higher detection probability and lower false alarm than that provided by the others.