  Digital fingerprinting is a framework for marking media files, such as
images, music, or movies, with user-specific signatures to deter illegal
distribution. Multiple users can collude to produce a forgery that can
potentially overcome a fingerprinting system. This paper proposes an
equiangular tight frame fingerprint design which is robust to such collusion
attacks. We motivate this design by considering digital fingerprinting in terms
of compressed sensing. The attack is modeled as linear averaging of multiple
marked copies before adding a Gaussian noise vector. The content owner can then
determine guilt by exploiting correlation between each user's fingerprint and
the forged copy. The worst-case error probability of this detection scheme is
analyzed and bounded. Simulation results demonstrate the average-case
performance is similar to the performance of orthogonal and simplex fingerprint
designs, while accommodating several times as many users.
