Automatic Detection of Internal Copy-Move Forgeries in Images

Abstract

This article presents an implementation and discussion of the recently proposed 'Efficient Dense-Field Copy-Move Forgery Detection' by Cozzolino et al. This method is a forgery detection based on a dense field of descriptors chosen to be invariant by rotation. Zernike moments were suggested in the original article. An efficient matching of the descriptors is then performed using PatchMatch, which is extremely efficient to find duplicate regions. Regions matched by PatchMatch are processed to find the final detections. This allows a precise and accurate detection of copy-move forgeries inside a single suspicious image. We also extend successfully the method to the use of dense SIFT descriptors and show that they are better at detecting forgeries using Poisson editing. Source Code The reviewed source code and documentation for this algorithm are available from the web page of this article 1. Compilation and usage instruction are included in the README.txt file of the archive.

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“…1) Efficient Dense-Field from Cozzolino et al [7]. During the evaluation, we use the implementation of Ehret [21]. Ehret released two versions of [7] using Zernike The detection map present two connected components with the same ID that overlaps more than one object from the ground-truth.…”