ID-Reveal: Identity-aware DeepFake Video Detection

State-of-the-art DeepFake forgery detectors are trained in a supervised fashion to answer the question 'is this video real or fake?'. Given that their training is typically method-specific, these approaches show poor generalization across different types of facial manipulations, e.g., face swapping or facial reenactment. In this work, we look at the problem from a different perspective by focusing on the facial characteristics of a specific identity; i.e., we want to answer the question 'Is this the person who is claimed to be?'. To this end, we introduce ID-Reveal, a new approach that learns temporal facial features, specific of how each person moves while talking, by means of metric learning coupled with an adversarial training strategy. Our method is independent of the specific type of manipulation since it is trained only on real videos. Moreover, relying on high-level semantic features, it is robust to widespread and disruptive forms of post-processing. We performed a thorough experimental analysis on several publicly available benchmarks, such as FaceForensics++, Google's DFD, and Celeb-DF. Compared to state of the art, our method improves generalization and is more robust to low-quality videos, that are usually spread over social networks. In particular, we obtain an average improvement of more than 15% in terms of accuracy for facial reenactment on high compressed videos.