SIFT-Symmetry: A robust detection method for copy-move forgery with reflection attack

Warif, Nor Bakiah Abd and Wahab, Ainuddin Wahid Abdul and Idris, Mohd Yamani Idna and Salleh, Rosli and Othman, Fazidah (2017) SIFT-Symmetry: A robust detection method for copy-move forgery with reflection attack. Journal of Visual Communication and Image Representation, 46. pp. 219-232. ISSN 1047-3203, DOI https://doi.org/10.1016/j.jvcir.2017.04.004.

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Official URL: https://doi.org/10.1016/j.jvcir.2017.04.004

Abstract

Copy-move forgery (CMF) is a popular image manipulation technique that is simple and effective in creating forged illustrations. The bulk of CMF detection methods concentrate on common geometrical transformation attacks (e.g., rotation and scale) and post-processing attacks (e.g., Joint Photographic Experts Group (JPEG) compression and Gaussian noise addition). However, geometrical transformation that involves reflection attacks has not yet been highlighted in the literature. As the threats of reflection attack are inevitable, there is an urgent need to study CMF detection methods that are robust against this type of attack. In this study, we investigated common geometrical transformation attacks and reflection-based attacks. Also, we suggested a robust CMF detection method, called SIFT-Symmetry, that innovatively combines the Scale Invariant Feature Transform (SIFT)-based CMF detection method with symmetry-based matching. We evaluated the SIFT-Symmetry with three established methods that are based on SIFT, multi-scale analysis, and patch matching using two new datasets that cover simple transformation and reflection-based attacks. The results show that the F-score of the SIFT-Symmetry method surpassed the average 80% value for all geometrical transformation cases, including simple transformation and reflection-based attacks, except for the reflection with rotation case which had an average F-score of 65.3%. The results therefore show that the SIFT-Symmetry method gives better performance compared to the other existing methods.

Item Type: Article
Funders: UNSPECIFIED
Uncontrolled Keywords: Blind detection; Copy-move forgery; Image forensics; Reflection detection
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 22 Oct 2019 06:23
Last Modified: 22 Oct 2019 06:23
URI: http://eprints.um.edu.my/id/eprint/22815

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