On the Robustness of Parametric Watermarking of Speech.

Parameter-embedded watermarking is effected through slight perturbations of parametric models of some deeply-integrated dynamics of a signal. This paper is concerned with particular model form, linear prediction (LP), which is naturally suited to the application of interest, speech watermarking. The focus of this paper is on the robustness performance of LP-embedded speech watermarking. It is shown that the technique is quite robust to a wide array of attacks including noise addition, cropping, compression, filtering, and others. In the LP formulation, a set-theoretic adjunct to the parameter embedding can be used to identify a watermark that is optimally robust against certain attacks, within a quantified fidelity constraint. [ABSTRACT FROM AUTHOR]