Efficient non-interactive zero-knowledge watermark detector robust to sensitivity attacks

Abstract

Zero-knowledge watermark detectors presented to date 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, an efficient zero-knowledge version of the Generalized Gaussian Maximum Likelihood (ML) detector is introduced. 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. Two versions of the zero-knowledge detector are presented; the first one makes use of two new zero-knowledge proofs for modulus and square root calculation; the second is an improved version applicable when the spreading sequence is binary, and it has minimum communication complexity. Completeness, soundness and zero-knowledge properties of the developed protocols are proved, and they are compared with previous zero-knowledge watermark detection protocols in terms of receiver operating characteristic, resistance to sensitivity attacks and communication complexity.