Copy-move forgery detection using combined features and transitive matching

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 f...

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Bibliographic Details
Published in:Multimedia tools and applications 2019-11, Vol.78 (21), p.30081-30096
Main Authors: Lin, Cong, Lu, Wei, Huang, Xinchao, Liu, Ke, Sun, Wei, Lin, Hanhui, Tan, Zhiyuan
Format: Article
Language:English
Subjects:
Affine transformations
Computer Communication Networks
Computer Science
Data management
Data Structures and Information Theory
Feature extraction
Filtration
Forensic computing
Forensic sciences
Forgery
Image filters
Image segmentation
Internet of Things
Matching
Multimedia
Multimedia Information Systems
Special Purpose and Application-Based Systems
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Summary: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.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-018-6922-4