Copy-move forgery detection using combined features and transitive matching

Lin, Cong; Lu, Wei; Huang, Xinchao; Liu, Ke; Sun, Wei; Lin, Hanhui; Tan, Zhiyuan

Abstract

Recently, the research of Internet of Things (IoT) and Multimedia Big Data (MBD) has been growing tremendously. Both IoT and MBD have a lot of multimedia data, which can be tampered easily. Therefore, the research of multimedia forensics is necessary. Copy-move is an important branch of multimedia forensics. In this paper, a novel copy-move forgery detection scheme using combined features and transitive matching is proposed. First, SIFT and LIOP are extracted as combined features from the input image. Second, transitive matching is used to improve the matching relationship. Third, a filtering approach using image segmentation is proposed to filter out false matches. Fourth, affine transformations are estimated between these image patches. Finally, duplicated regions are located based on those affine transformations. The experimental results demonstrate that the proposed scheme can achieve much better detection results on the public database under various attacks.

Citation

Lin, C., Lu, W., Huang, X., Liu, K., Sun, W., Lin, H., & Tan, Z. (2019). Copy-move forgery detection using combined features and transitive matching. Multimedia Tools and Applications, 78(21), 30081-30096. https://doi.org/10.1007/s11042-018-6922-4

• Journal Article Type: Article
• Acceptance Date: Nov 19, 2018
• Online Publication Date: Nov 28, 2018
• Publication Date: 2019-11
• Deposit Date: Nov 29, 2018
• Publicly Available Date: Dec 17, 2018
• Journal: Multimedia Tools and Applications
• Print ISSN: 1380-7501
• Electronic ISSN: 1573-7721
• Publisher: BMC
• Peer Reviewed: Peer Reviewed
• Volume: 78
• Issue: 21
• Pages: 30081-30096
• DOI: https://doi.org/10.1007/s11042-018-6922-4
• Keywords: Media Technology; Computer Networks and Communications; Hardware and Architecture; Software
• Public URL: http://researchrepository.napier.ac.uk/Output/1401300