SS-SVD: Spread spectrum data hiding scheme based on Singular Value Decomposition

In recent years, the necessity to protect multimedia content from illegal copying has been made more critical by the advent of digital technology. Singular Value Decomposition (SVD) received much attention from the watermarking community due to its appealing properties, such as the invariance of singular values (SVs) to common image processing operations and geometric transforms, like rotation, translation and scaling. However, most of watermarking schemes require a lot of prior information to detect the watermark and in addition they are not secure. In this paper we investigate the efficiency of optimal decoding schemes for the watermark inserted into the SVD domain of the host images using an additive spread spectrum (SS) embedding framework. In order to use the singular values (SVs) with the SS embedding we adopt several restrictions that ensure that the values of the SVs remain real, positive and sorted. For both the optimal maximum likelihood decoder and sub-optimal decoders we assume that the PDF of SVs can be modeled by the Weibull distribution. Furthermore, we investigate the error probability behavior, i.e., the probability of detection and the probability of false detection, for the applied optimal decoders. By taking into account the efficiency and the necessary auxiliary information for decoding the watermark, we discuss the suitable decoder for various operating situations.