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 realism 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. We show that the use of additional domain specific knowledge improves forgery detection to an unprecedented accuracy, even in the presence of strong compression. By conducting a series of thorough experiments, we quantify the differences between classical approaches, novel deep learning approaches, and the performance of human observers.
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