SVD-based digital image watermarking on approximated orthogonal matrix

Yevhen Zolotavkin, Martti Juhola

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

2 Citations (Scopus)

Abstract

A new watermarking method based on Singular Value Decomposition is proposed in this paper. The method uses new embedding rules to store a watermark in orthogonal matrix that is preprocessed in advance in order to fit a proposed model of orthogonal matrix. Some experiments involving common distortions for grayscale images were done in order to confirm efficiency of the proposed method. The robustness of watermark embedded by our method was higher for all the proposed rules under condition of jpeg compression and in some cases outperformed existing method for more than 46%.
Original languageEnglish
Title of host publicationProceedings of the 10th International Conference on Security and Cryptography (SECRYPT 2013)
Subtitle of host publicationReykjavik, Iceland, 29 - 31 July, 2-13
EditorsPierangela Samarati
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages10
ISBN (Print)9789897581311
Publication statusPublished - 2013
Externally publishedYes
EventInternational Conference on Information Security and Cryptography 2013 - Reykjavik, Iceland
Duration: 29 Jul 201331 Jul 2013
Conference number: 10th
http://www.secrypt.icete.org/?y=2013

Conference

ConferenceInternational Conference on Information Security and Cryptography 2013
Abbreviated titleSECRYPT 2013
Country/TerritoryIceland
CityReykjavik
Period29/07/1331/07/13
OtherSECRYPT is part of ICETE, the 10th International Joint Conference on e-Business and Telecommunications.
Registration to SECRYPT allows free access to all other ICETE conferences.

ICETE 2013 will be held in conjunction with DATA 2013, ICSOFT 2013 and SIMULTECH 2013.
Registration to ICETE allows free access to the DATA, ICSOFT and SIMULTECH conferences (as a non-speaker).
Internet address

Keywords

  • Digital image watermarking
  • Singular value decomposition
  • Robustness
  • Distortions
  • Transparency

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