TY - CHAP
T1 - Independent component analysis for domain independent watermarking
AU - Bounkong, Stephane
AU - Saad, David
AU - Lowe, David
N1 - The original publication is available at www.springerlink.com
PY - 2002/1/1
Y1 - 2002/1/1
N2 - A new principled domain independent watermarking framework is presented. The new approach is based on embedding the message in statistically independent sources of the covertext to mimimise covertext distortion, maximise the information embedding rate and improve the method's robustness against various attacks. Experiments comparing the performance of the new approach, on several standard attacks show the current proposed approach to be competitive with other state of the art domain-specific methods.
AB - A new principled domain independent watermarking framework is presented. The new approach is based on embedding the message in statistically independent sources of the covertext to mimimise covertext distortion, maximise the information embedding rate and improve the method's robustness against various attacks. Experiments comparing the performance of the new approach, on several standard attacks show the current proposed approach to be competitive with other state of the art domain-specific methods.
KW - watermarking framework
KW - covertext distortion
KW - information embedding rate
UR - http://www.scopus.com/inward/record.url?scp=84902190526&partnerID=8YFLogxK
UR - http://www.springerlink.com/content/vl7h077thv8qbevj/
U2 - 10.1007/3-540-46084-5_83
DO - 10.1007/3-540-46084-5_83
M3 - Chapter
SN - 9783540440741
VL - 2415
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 510
EP - 515
BT - Artificial Neural Networks — ICANN 2002
A2 - Dorronso, Jose R.
PB - Springer
CY - Berlin
T2 - Artificial Neural Networks 2002
Y2 - 28 August 2002 through 30 August 2002
ER -