Performance of orthogonal fingerprinting codes under worst-case noise

Negar Kiyavash; Pierre Moulin

doi:10.1109/TIFS.2009.2026462

Performance of orthogonal fingerprinting codes under worst-case noise

Research output: Contribution to journal › Article › peer-review

Abstract

We study the effect of the noise distribution on the error probability of the detection test when a class of randomly rotated spherical fingerprints is used. The detection test is performed by a focused correlation detector, and the spherical codes studied here form a randomized orthogonal constellation. The colluders create a noise-free forgery by uniform averaging of their individual copies, and then add a noise sequence to form the actual forgery. We derive the noise distribution that maximizes the error probability of the detector under average and almost-sure distortion constraints. Moreover, we characterize the noise distribution that minimizes the decoder's error exponent under a large-deviations distortion constraint.

Original language	English (US)
Article number	5159463
Pages (from-to)	293-301
Number of pages	9
Journal	IEEE Transactions on Information Forensics and Security
Volume	4
Issue number	3
DOIs	https://doi.org/10.1109/TIFS.2009.2026462
State	Published - Sep 2009

Keywords

Collusion attacks
Fingerprinting
Noise

ASJC Scopus subject areas

Safety, Risk, Reliability and Quality
Computer Networks and Communications

Online availability

10.1109/TIFS.2009.2026462

Library availability

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title = "Performance of orthogonal fingerprinting codes under worst-case noise",

abstract = "We study the effect of the noise distribution on the error probability of the detection test when a class of randomly rotated spherical fingerprints is used. The detection test is performed by a focused correlation detector, and the spherical codes studied here form a randomized orthogonal constellation. The colluders create a noise-free forgery by uniform averaging of their individual copies, and then add a noise sequence to form the actual forgery. We derive the noise distribution that maximizes the error probability of the detector under average and almost-sure distortion constraints. Moreover, we characterize the noise distribution that minimizes the decoder's error exponent under a large-deviations distortion constraint.",

keywords = "Collusion attacks, Fingerprinting, Noise",

author = "Negar Kiyavash and Pierre Moulin",

note = "Funding Information: Manuscript received May 12, 2008; revised June 11, 2009. First published July 07, 2009; current version published August 14, 2009. This work was supported by the National Science Foundation under Grant CCR03-25924 and Grant CCF 0729061. This work was presented in part at ICASSP{\textquoteright}06 and SSP{\textquoteright}07. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Gaurav Sharma.",

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N2 - We study the effect of the noise distribution on the error probability of the detection test when a class of randomly rotated spherical fingerprints is used. The detection test is performed by a focused correlation detector, and the spherical codes studied here form a randomized orthogonal constellation. The colluders create a noise-free forgery by uniform averaging of their individual copies, and then add a noise sequence to form the actual forgery. We derive the noise distribution that maximizes the error probability of the detector under average and almost-sure distortion constraints. Moreover, we characterize the noise distribution that minimizes the decoder's error exponent under a large-deviations distortion constraint.

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Performance of orthogonal fingerprinting codes under worst-case noise

Abstract

Keywords

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