.png){kind=icon}
.png){kind=icon}
Return
Regular Simplex Fingerprints and Their Optimality Properties
.png){kind=icon}
PRE
AI
DOI：10.1109/TIFS.2009.2025855.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper addresses the design of additive fingerprints that are maximally resilient against linear collusion attacks on a focused correlation detector, as defined below. Let N be the length of the host vector and M <= N + 1 the number of users. The focused detector performs a correlation test in order to decide whether a user of interest is among the colluders. Both the fingerprint embedder and the colluders are subject to squared-error distortion constraints. We show that simplex fingerprints maximize a geometric figure of merit for this detector. In that sense they outperform orthogonal fingerprints but the advantage vanishes asM -> infinity. They are also optimal in terms of minimizing the probability of error of the focused detector when the attack is a uniform averaging of the marked copies followed by the addition of white Gaussian noise. Reliable detection is guaranteed provided that the number of colluders K << root N. Moreover, we study the probability of error performance of simplex fingerprints for the focused correlation detector when the colluders use nonuniform averaging plus white Gaussian noise attacks.
Keywords:
Collusion attacks
fingerprinting
signal detection
simplex codes
Journal
IF:
8
Papers:
5.2K
Citations:
2.3W
