[IPOL announce] new article: Analysis and Experimentation on the ManTraNet Image Forgery Detector

[announcements about the IPOL journal]

A new article is available in IPOL: https://www.ipol.im/pub/art/2022/431/

Quentin Bammey,
Analysis and Experimentation on the ManTraNet Image Forgery Detector,
Image Processing On Line, 12 (2022), pp. 457–468.
https://doi.org/10.5201/ipol.2022.431


Abstract
This work describes the ManTraNet network for image forgery detection. 
ManTraNet is an end-to-end convolutional neural network composed of two 
sub-networks, one to extract features linked to traces of manipulation, 
and another to detect local anomalies between the features. It is 
trained on pristine and forged images from several datasets. We briefly 
analyze the results provided by ManTraNet, so as to highlight its 
qualities and limitations. Overall, ManTraNet yields state-of-the-art 
results on benchmark datasets with images similar to the one it sees in 
training, but is unreliable on wild images, due to its opacity and the 
difficulty distinguishing true detections from false positives.