Independent component analysis for domain independent watermarking

Stephane Bounkong, David Saad, David Lowe

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

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.
Original languageEnglish
Title of host publicationArtificial Neural Networks — ICANN 2002
EditorsJose R. Dorronso
Place of PublicationBerlin
PublisherSpringer
Pages510-515
Number of pages6
Volume2415
ISBN (Print)9783540440741
DOIs
Publication statusPublished - 1 Jan 2002
EventArtificial Neural Networks 2002 - Madrid, Spain
Duration: 28 Aug 200230 Aug 2002

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2415 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

ConferenceArtificial Neural Networks 2002
Abbreviated titleICANN 2002
CountrySpain
CityMadrid
Period28/08/0230/08/02

Fingerprint

Independent component analysis
Independent Component Analysis
Watermarking
Attack
Experiments
Maximise
Robustness
Experiment
Framework
Standards

Bibliographical note

The original publication is available at www.springerlink.com

Keywords

  • watermarking framework
  • covertext distortion
  • information embedding rate

Cite this

Bounkong, S., Saad, D., & Lowe, D. (2002). Independent component analysis for domain independent watermarking. In J. R. Dorronso (Ed.), Artificial Neural Networks — ICANN 2002 (Vol. 2415, pp. 510-515). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2415 LNCS). Berlin: Springer. https://doi.org/10.1007/3-540-46084-5_83
Bounkong, Stephane ; Saad, David ; Lowe, David. / Independent component analysis for domain independent watermarking. Artificial Neural Networks — ICANN 2002. editor / Jose R. Dorronso. Vol. 2415 Berlin : Springer, 2002. pp. 510-515 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Bounkong, S, Saad, D & Lowe, D 2002, Independent component analysis for domain independent watermarking. in JR Dorronso (ed.), Artificial Neural Networks — ICANN 2002. vol. 2415, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2415 LNCS, Springer, Berlin, pp. 510-515, Artificial Neural Networks 2002, Madrid, Spain, 28/08/02. https://doi.org/10.1007/3-540-46084-5_83

Independent component analysis for domain independent watermarking. / Bounkong, Stephane; Saad, David; Lowe, David.

Artificial Neural Networks — ICANN 2002. ed. / Jose R. Dorronso. Vol. 2415 Berlin : Springer, 2002. p. 510-515 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2415 LNCS).

Research output: Chapter in Book/Report/Conference proceedingChapter

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Bounkong S, Saad D, Lowe D. Independent component analysis for domain independent watermarking. In Dorronso JR, editor, Artificial Neural Networks — ICANN 2002. Vol. 2415. Berlin: Springer. 2002. p. 510-515. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/3-540-46084-5_83