Efficient Zero-Knowledge Watermark Detection with Improved Robustness to Sensitivity Attacks

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. This detector has shown an improved resilience against sensitivity attacks, that is empirically corroborated in the present work. Two versions of the zero-knowledge detector are presented; the first one makes use of two new zero-knowledge proofs for absolute value 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.