Audio watermarking based on quantization in wavelet domain

Vivekananda Bhat K., Indranil Sengupta, Abhijit Das

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Citations (Scopus)

Abstract

A robust and oblivious audio watermarking based on quantization of wavelet coefficients is proposed in this paper. Watermark data is embedded by quantizing large wavelet coefficient in absolute value of high frequency detail sub-band at the third level wavelet transform. The watermark can be extracted without using the host signal. Experimental results show that the proposed method has good imperceptibility and robustness under common signal processing attacks such as additive noise, low-pass filtering, re-sampling, re-quantization, and MP3 compression. Moreover it is also robust against desynchronization attacks such as random cropping and jittering. Performance of the proposed scheme is better than audio watermarking scheme based on mean quantization.

Original languageEnglish
Title of host publicationInformation Systems Security - 4th International Conference, ICISS 2008, Proceedings
Pages235-242
Number of pages8
DOIs
Publication statusPublished - 01-12-2008
Event4th International Conference on Information Systems Security, ICISS 2008 - Hyderabad, India
Duration: 16-12-200820-12-2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5352 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th International Conference on Information Systems Security, ICISS 2008
CountryIndia
CityHyderabad
Period16-12-0820-12-08

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Bhat K., V., Sengupta, I., & Das, A. (2008). Audio watermarking based on quantization in wavelet domain. In Information Systems Security - 4th International Conference, ICISS 2008, Proceedings (pp. 235-242). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5352 LNCS). https://doi.org/10.1007/978-3-540-89862-7_20