FaceForensics : Learning to Detect Manipulated Facial Images (ICCV 2019)

The rapid progress in synthetic image generation and manipulation has now come to a point where it raises significant concerns on the implication on the society. At best,this leads to a loss of trust in digital content, but it might even cause further harm by spreading false information and the creation of fake news. In this paper, we examine the real-ism of state-of-the-art image manipulations, and how difficult it is to detect them – either automatically or by humans.In particular, we focus on DeepFakes, Face2Face, and FaceSwap as prominent representatives for facial manipulations. We create more than half a million manipulated images respectively for each approach. The resulting publicly available dataset is at least an order of magnitude larger than comparable alternatives and it enables us to train data-driven forgery detectors in a supervised fashion.