Local JPEG Grid Detector via Blocking Artifacts, a Forgery Detection Tool
Tina Nikoukhah, Miguel Colom, Jean-Michel Morel, Rafael Grompone von Gioi
→ BibTeX
@article{ipol.2020.283,
    title   = {{Local JPEG Grid Detector via Blocking Artifacts, a Forgery Detection Tool}},
    author  = {Nikoukhah, Tina and Colom, Miguel and Morel, Jean-Michel and Grompone von Gioi, Rafael},
    journal = {{Image Processing On Line}},
    volume  = {10},
    pages   = {24--42},
    year    = {2020},
    doi     = {10.5201/ipol.2020.283},
}
% if your bibliography style doesn't support doi fields:
    note    = {\url{https://doi.org/10.5201/ipol.2020.283}}
published
2020-05-21
reference
Tina Nikoukhah, Miguel Colom, Jean-Michel Morel, and Rafael Grompone von Gioi, Local JPEG Grid Detector via Blocking Artifacts, a Forgery Detection Tool, Image Processing On Line, 10 (2020), pp. 24–42. https://doi.org/10.5201/ipol.2020.283

Communicated by Miguel Colom
Demo edited by Tina Nikoukhah

Abstract

Image JPEG compression leaves blocking artifact traces. This paper describes an algorithm that exploits those traces to locally recover the grid embedded in the image by the JPEG compression. The algorithm returns a list of grids associated with different parts of the image. The method uses Chen and Hsu's cross-difference to reveal the artifacts. Then, an a contrario validation step according to Desolneux, Moisan and Morel's theory delivers for each detected grid a Number of False Alarms (NFA) which tells how unlikely it is that the detection is due to chance. The only parameter is the step size of the windows used, which represents the exhaustiveness of the method. The application to image forgery detection is twofold: first, the presence of discrepant JPEG grids with low NFA is a strong forgery cue; second, knowledge of the grid is anyway required for further JPEG forensic analysis.

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