Analysis and Experimentation on the ManTraNet Image Forgery Detector

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.