Multi-scale feature extraction and adaptive matching for copy-move forgery detection

Xiu Li Bi, Chi Man Pun, Xiao Chen Yuan

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)


A copy-move forgery detection scheme by using multi-scale feature extraction and adaptive matching is proposed in this paper. First, the host image is segmented into the non-overlapping patches of irregular shape in different scales. Then, Scale Invariant Feature Transform is applied to extract feature points from all patches, to generate the multi-scale features. An Adaptive Patch Matching algorithm is subsequently proposed for finding the matching that indicate the suspicious forged regions in each scale. Finally, the suspicious regions in all scales are merged to generate the detected forgery regions in the proposed Matched Keypoints Merging algorithm. Experimental results show that the proposed scheme performs much better than the existing state-of-the-art copy-move forgery detection algorithms, even under various challenging conditions, including the geometric transforms, such as scaling and rotation, and the common signal processing, such as JPEG compression and noise addition; in addition, the special cases such as the multiple copies and the down-sampling are also evaluated, the results indicate the very good performance of the proposed scheme.

Original languageEnglish
Pages (from-to)363-385
Number of pages23
JournalMultimedia Tools and Applications
Issue number1
Publication statusPublished - 1 Jan 2018
Externally publishedYes


  • Adaptive Patch Matching
  • Copy-Move Forgery Detection
  • Multi-Scale Feature Extraction


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