zero-knowledge watermark detector robust to sensitivity attacks
Current zero-knowledge watermark detectors are based on a linear correlation between the asset features and a given secret sequence.This detection function is susceptible of being attacked by sensitivity attacks,for which zero-knowledge does not provide protection.In this paper a new zero-knowledge watermark detector robust to sensitivity attacks is presented,using the Generalized Gaussian Maximum Likelihood (ML)detector as basis.The inherent robustness that this detector presents against sensitivity attacks,together with the security provided by the zero-knowledge protocol that conceals the keys that could be used to remove the watermark or to produce forged assets,results in a robust and secure protocol.Additionally,two new zero-knowledge proofs for modulus and square root calculation are presented;they serve as building blocks for the zero-knowledge implementation of the Generalized Gaussian ML detector,and also open new possibilities in the design of high level protocols.
zero-knowledge sensitivity attacks watermark detection generalized gaussian detector