Automatic Detection of Internal Copy-Move Forgeries in Images
Thibaud Ehret
→ BibTeX
    title   = {{Automatic Detection of Internal Copy-Move Forgeries in Images}},
    author  = {Ehret, Thibaud},
    journal = {{Image Processing On Line}},
    volume  = {8},
    pages   = {167--191},
    year    = {2018},
    doi     = {10.5201/ipol.2018.213},
% if your bibliography style doesn't support doi fields:
    note    = {\url{}}
Thibaud Ehret, Automatic Detection of Internal Copy-Move Forgeries in Images, Image Processing On Line, 8 (2018), pp. 167–191.

Communicated by Loïc Simon
Demo edited by Thibaud Ehret


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.