Maximum likelihood detection in image watermarking using generalized gamma model

Abstract

Digital image watermark can be detected in transform domain using maximum likelihood (ML) detection, whereby the decision threshold is obtained using the NeymanPearson criterion. A probability distribution function (PDF) is required to correctly model the statistical behavior of the transform coefficients. In the literature, this detection method has been considered by modeling magnitude of a set of discrete Fourier transform (DFT) coefficients using a Weibull PDF. In this paper, we propose extending the Weibull model to a generalized gamma model. For the work here, we also propose new estimators for parameters of the generalized gamma PDF. © 2005 IEEE.