RST invariant multi view 3D image watermarking using DWT and SVD

Sibaji Gaj, Shuvendu Rana, Anirban Lekharu, Arijit Sur, Prabin Kumar Bora

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

2 Citations (Scopus)

Abstract

In this paper, a multi-view stereo image watermarking scheme is proposed to resist the RST (rotation, scaling and translation) attack. To make the scheme resilient to RST, the coefficients of Singular Value Decomposition (SVD) from both left and right views have been used for insertion of the watermark bits. 2D-DWT (Discrete wavelet transform) is used as a preprocessing step to get more correlated SVD coefficients of the left and right view such that the visual degradation due to embedding can be reduced. In this work, a blind embedding scheme is proposed by altering the selected SVD coefficients to improve the robustness of the embedding scheme. A comprehensive set of experiments have been performed to justify the robustness of the proposed scheme against RST attack. Moreover, this scheme can be used to detect the view swapping attack using DIBR technique.

Original languageEnglish
Title of host publication2015 5th National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, NCVPRIPG 2015
Place of PublicationPiscataway, NJ
PublisherIEEE
Number of pages4
ISBN (Electronic)9781467385640
DOIs
Publication statusPublished - 13 Jun 2016
Event5th National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, NCVPRIPG 2015 - Patna, Bihar, India
Duration: 16 Dec 201519 Dec 2015

Conference

Conference5th National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, NCVPRIPG 2015
Country/TerritoryIndia
CityPatna, Bihar
Period16/12/1519/12/15

Keywords

  • watermarking
  • three-dimensional displays
  • discrete wavelet transforms
  • robustness
  • matrix decomposition
  • resists

Fingerprint

Dive into the research topics of 'RST invariant multi view 3D image watermarking using DWT and SVD'. Together they form a unique fingerprint.

Cite this