Deepfake Detection Via Separable Self-Consistency Learning
Lin Lu, Yunhong Wang, Wenqi Zhuo, Liang Zhang, Guangshuai Gao, Yuanfang Guo · 2024
Deepfake detection technologies have been developed rapidly in recent years, due to the potential severe security threats induced by the realistic deep facial forgeries. Among the existing deepfake detection methods, self-supervised methods have drawn significant attentions from researchers, because of their better generalization ability against the deep forgeries produced via unseen deepfake techniques. Unfortunately, existing state-of-the-art self-supervised approaches have not properly considered that different pairs of patches from different regions actually give different contributions. Thus, their learned representations are coarse and the generalization performances are less decent. In this paper, we propose a new self-supervised deepfake detection method, named deepfake detection via separable self-consistency learning (SSCLDFD), to improve the generalization ability of deepfake detection. Specifically, to effectively extract detection features, we construct a multi-scale Texture Enhanced Feature Extraction Network (TEFEN), by forming a Central-Difference based Convolution Module (CDCM) to enhance the texture information, which contain rich forgery cues. Since different pairs of patches from different regions (i.e. background and facial regions) tend to give various consistencies, we propose a separable self-consistency loss to explicitly constrain the representation learning. Extensive experiments demonstrate that our SSCL-DFD can give superior generalization performances compared to the state-of-the-art methods.