Contrastive Learning for DeepFake Classification and Localization via Multi-Label Ranking

We propose a unified approach to simultaneously addressing the conventional setting of binary deepfake classification and a more challenging scenario of uncovering what facial components have been forged as well as the exact order of the manipulations. To solve the former task, we consider multiple instance learning (MIL) that takes each image as a bag and its patches as instances. A positive bag corresponds to a forged image that includes at least one manipulated patch (i.e., a pixel in the feature map). The formulation enables us to estimate the probability of an input image being a fake one and to establish the corresponding contrastive MIL loss. On the other hand, tackling the component-wise deepfake problem can be reduced to solving multi-label prediction, but the requirement to recover the manipulation order further complicates the learning task into a multi-label ranking problem. We resolve this difficulty by designing a tailor-made loss term to enforce that the rank order of the predicted multi-label probabilities respects the ground-truth order of the sequential modifications of a deepfake image. For experiments and comparisons with other relevant techniques, we provide extensive results and ablation studies to demonstrate that the proposed method is an overall more comprehensive solution to deepfake detection.