Performance of orthogonal fingerprinting codes under worst-case noise
IEEE Transactions on Information Forensics and Security
Regular simplex fingerprints and their optimality properties
IEEE Transactions on Information Forensics and Security
High-rate random-like spherical fingerprinting codes with linear decoding complexity
IEEE Transactions on Information Forensics and Security - Special issue on electronic voting
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This paper proposes codes that achieve the fundamental capacity limits of digital fingerprinting subject to mean-squared distortion constraints on the fingerprint embedder and the colluders. We first show that the traditional method of fingerprint decoding by thresholding correlation statistics falls short of this goal: reliable performance is impossible at code rates greater than some value C1 that is strictly less than capacity. To bridge the gap to capacity, a more powerful decoding method is needed. The Maximum Penalized Gaussian Mutual Information decoder presented here meets this requirement. Finally, a mathematical framework and a capacity expression for fingerprinting of social networks are presented.