TY - GEN
T1 - A high-rate fingerprinting code
AU - Jourdas, Jean François
AU - Moulin, Pierre
PY - 2008
Y1 - 2008
N2 - 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%).
AB - 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%).
KW - Fingerprinting code
UR - http://www.scopus.com/inward/record.url?scp=42949140390&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=42949140390&partnerID=8YFLogxK
U2 - 10.1117/12.767850
DO - 10.1117/12.767850
M3 - Conference contribution
AN - SCOPUS:42949140390
SN - 9780819469915
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Proceedings of SPIE-IS and T Electronic Imaging - Security, Forensics, Steganography, and Watermarking of Multimedia Contents X
T2 - Security, Forensics, Steganography, and Watermarking of Multimedia Contents X
Y2 - 28 January 2008 through 30 January 2008
ER -