Fingerprinting with Equiangular Tight Frames

TL;DR

This paper introduces an equiangular tight frame fingerprinting scheme that enhances robustness against collusion attacks in digital media, offering improved user capacity while maintaining detection performance.

Contribution

It proposes a novel equiangular tight frame design for digital fingerprinting, modeled via compressed sensing, to improve collusion resistance and increase user capacity.

Findings

Performance comparable to orthogonal and simplex designs

Supports several times more users than traditional methods

Provides bounds on worst-case error probability

Abstract

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.