Perceptual audio watermarking by learning in wavelet domain

Bilge Gunsel*, Serap Kirbiz

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

Conventional blind watermark (WM) decoding schemes use correlation-based decision rules because of their simplicity. Drawback of the correlator decoders is their performance relies on the decision threshold. Existence of an undesirable correlation between the WM data embedded through a secret key and the host signal makes the decision threshold specification harder, especially in noisy channels. To overcome this drawback, we propose a SVM-based decoding scheme which is capable of learning the embedded WM data in wavelet domain. It is shown that both decoding and detection performance of the introduced WM extraction technique outperforms state-of-the-art correlation-based schemes. Test results demonstrate that learning in the wavelet domain improves robustness to attacks while reducing complexity.

Original languageEnglish
Title of host publicationProceedings - 18th International Conference on Pattern Recognition, ICPR 2006
Pages383-386
Number of pages4
DOIs
Publication statusPublished - 2006
Event18th International Conference on Pattern Recognition, ICPR 2006 - Hong Kong, China
Duration: 20 Aug 200624 Aug 2006

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume3
ISSN (Print)1051-4651

Conference

Conference18th International Conference on Pattern Recognition, ICPR 2006
Country/TerritoryChina
CityHong Kong
Period20/08/0624/08/06

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