The prevalence of facial recognition, biometric unlock, and social media presents a significant opportunity for bad actors to introduce forged or manipulated images to spread false information or damage reputations. This is aided by the continuing improvement in realistic image synthesis and manipulation by generative adversarial network, GAN, based methods. We introduce a method for the detection of forged images and localization of manipulated regions in face images.
To simplify the localization of manipulated regions, we develop the manipulation appearance model, MAM. This utilizes basis templates for commonly manipulated regions. Instead of estimating the entire localization map, MAM can predict a 10-d vector that is combined with the templates to produce a manipulation mask for the image.
DFFD dataset download page: http://cvlab.cse.msu.edu/dffd-dataset.html
Detection of Digitally Manipulated Faces Source Code
The source code for the networks is available at: https://github.com/JStehouwer/FFD_CVPR2020