Abstract

Digital fingerprinting is a technique for identifying users who might try to use multimedia content for unintended purposes, such as redistribution. These fingerprints are typically embedded into the content using watermarking techniques that are designed to be robust to a variety of attacks. A cost-e#ective attack against such digital fingerprints is collusion, where several di#erently marked copies of the same content are combined to disrupt the underlying fingerprints. In this paper, we investigate the problem of designing fingerprints that can withstand collusion and allow for the identification of colluders. We begin by introducing the collusion problem for additive embedding. We then study the e#ect that averaging collusion has upon orthogonal modulation. We introduce an e#cient detection algorithm for identifying the fingerprints associated with K colluders that requires log(n/K)) correlations for a group of n users. We next develop a fingerprinting scheme based upon code modulation that does not require as many basis signals as orthogonal modulation. We propose a new class of codes, called anti-collusion codes (ACC), which have the property that the composition of any subset of K or fewer codevectors is unique. Using this property, we can therefore identify groups of K or fewer colluders. We present a construction of binary-valued ACC under the logical AND operation that uses the theory of combinatorial designs and is suitable for both the on-o# keying and antipodal form of binary code modulation. In order to accommodate n users, our code construction requires only # n) orthogonal signals for a given number of colluders. We introduce four di#erent detection strategies that can be used with our ACC for identifying a suspect set of colluders. We demonstrate th...

Citations