An introduction to signal detection and estimation (2nd ed.)
An introduction to signal detection and estimation (2nd ed.)
Collusion-Secure Fingerprinting for Digital Data (Extended Abstract)
CRYPTO '95 Proceedings of the 15th Annual International Cryptology Conference on Advances in Cryptology
A note on the limits of collusion-resistant watermarks
EUROCRYPT'99 Proceedings of the 17th international conference on Theory and application of cryptographic techniques
Anti-collusion forensics of multimedia fingerprinting using orthogonal modulation
IEEE Transactions on Image Processing
Performance of orthogonal fingerprinting codes under worst-case noise
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
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
| Hi-index | 0.00 |
This paper addresses the design of additive fingerprints that are maximally resilient against Gaussian averaging collusion attacks. The detector performs a binary hypothesis test in order to decide whether a user of interest is among the colluders. The encoder (fingerprint designer) is to imbed additive fingerprints that minimize the probability of error of the test. Both the encoder and the attackers are subject to squared-error distortion constraints. We show that n-simplex fingerprints are optimal in sense of maximizing a geometric figure of merit for the detection test; these fingerprints outperform orthogonal fingerprints. They are also optimal in terms of maximizing the error exponent of the detection test, and maximizing the deflection criteria at the detector when the attacker’s noise is non-Gaussian. Reliable detection is guaranteed provided that the number of colluders $K \ll \sqrt{N}$, where N is the length of the host vector.