Research Output

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 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.

  • Type:


  • Date:

    28 November 2018

  • Publication Status:


  • Publisher

    Springer Nature

  • DOI:


  • Cross Ref:


  • ISSN:


  • Library of Congress:

    QA76 Computer software

  • Dewey Decimal Classification:

    005.8 Data security

  • Funders:

    National Natural Science Foundation of China


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.



Media Technology; Computer Networks and Communications; Hardware and Architecture; Software

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