KEYWORDS: Signal to noise ratio, Modulation, Distortion, Multimedia, Wavelets, Image compression, Transform theory, Monte Carlo methods, Computer simulations, Demodulation
In fingerprinting, a signature, unique to each user, is embedded in each distributed copy of a multimedia content,
in order to identify potential illegal redistributors. This paper investigates digital fingerprinting problems
involving millions of users and a handful of colluders. In such problems the rate of the fingerprinting code is
often well below fingerprinting capacity, and the use of codes with large minimum distance emerges as a natural
design. However, optimal decoding is a formidable computational problem. We investigate a design based on
a Reed-Solomon outer code modulated onto an orthonormal constellation, and the Guruswami-Sudan decoding
algorithm. We analyze the potential and limitations of this scheme and assess its performance by means of
Monte-Carlo simulations. In the second part of this paper, we apply this scheme to a blind image fingerprinting
problem, using a linear cancellation technique for embedding in the wavelet domain. Dramatic improvements
are obtained over previous blind image fingerprinting algorithms.
KEYWORDS: Monte Carlo methods, Computer programming, Signal to noise ratio, Forward error correction, Distortion, Computer simulations, Failure analysis, Multimedia, Device simulation, Video
In fingerprinting, a signature, unique to each user, is embedded in each distributed copy of a multimedia content,
in order to identify potential illegal redistributors. As an alternative to the vast majority of fingerprinting codes
built upon error-correcting codes with a high minimum distance, we propose the construction of a random-like
fingerprinting code, intended to operate at rates close to fingerprinting capacity. For such codes, the notion
of minimum distance has little relevance. As an example, we present results for a length 288,000 code that
can accommodate 33 millions of users and 50 colluders against the averaging attack. The encoding is done
by interleaving the users' identifying bitstrings and encoding them multiple times with recursive systematic
convolutional codes. The decoding is done in two stages. The first outputs a small set of possible colluders
using a bank of list Viterbi decoders. The second stage prunes this set using correlation decoding. We study
this scheme and assess its performance through Monte-Carlo simulations. The results show that at rates ranging
from 30% to 50% of capacity, we still have a low error probability (e.g. 1%).
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