Coded spread spectrum watermarking scheme

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

4 Citations (Scopus)

Abstract

In conventional spread spectrum watermarking schemes, random sequences are used for the modulation of watermark information. However, because of the mutual interference among those sequences, it requires complicated removal operation to improve the performance. In this paper, we propose an efficient spread spectrum watermarking scheme by introducing the CDMA technique at the modulation of watermark information. In order to control the energy assigned to spread spectrum sequences, we propose a coded method that encodes watermark information into a constant weight codeword. If the weight and its code-length are properly selected, the performance of the method could outperform the conventional methods.

Original languageEnglish
Title of host publicationDigital Forensics and Watermaking - 11th International Workshop, IWDW 2012, Revised Selected Papers
Pages169-183
Number of pages15
DOIs
Publication statusPublished - Sep 3 2013
Externally publishedYes
Event11th International Workshop on Digital Forensics and Watermaking, IWDW 2012 - Shanghai, China
Duration: Oct 31 2012Nov 3 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7809 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other11th International Workshop on Digital Forensics and Watermaking, IWDW 2012
CountryChina
CityShanghai
Period10/31/1211/3/12

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Coded spread spectrum watermarking scheme'. Together they form a unique fingerprint.

  • Cite this

    Kuribayashi, M. (2013). Coded spread spectrum watermarking scheme. In Digital Forensics and Watermaking - 11th International Workshop, IWDW 2012, Revised Selected Papers (pp. 169-183). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7809 LNCS). https://doi.org/10.1007/978-3-642-40099-5_15