Facial Region-based Ensembling for Unsupervised Temporal Deepfake Localization

This paper addresses the challenge of temporal deepfake localization. Instead of classifying entire videos as real or fake, the goal is isolating forged frames in untrimmed videos that might be partially manipulated. Recently, few deepfake localization methods have emerged. They are mostly supervised, therefore relying on costly annotations and suffering from a lack of generalization to unseen manipulations. As an alternative, we propose reformulating deepfake localization as an unsupervised time-series anomaly detection problem. Hence, to investigate the relevance of the proposed formulation, recent state-of-the-art techniques in anomaly detection for time-series are evaluated in the context of deepfake localization. To avoid using large architectures, geometric representations, e.g., facial landmarks, are used as input. Moreover, a facial-region based ensembling strategy is introduced for a better modelling of localized deepfake artifacts. Experiments performed on the ForgeryNet dataset demonstrate the effectiveness of the proposed ensembling method and highlight the suitability of the suggested formulation.