Forgery-aware Adaptive Vision Transformer for Face Forgery Detection

Abstract

With the advancement in face manipulation technologies, the importance of face forgery detection in protecting authentication integrity becomes increasingly evident. Previous Vision Transformer (ViT)-based detectors have demonstrated subpar performance in cross-database evaluations, primarily because fully fine-tuning with limited Deepfake data often leads to forgetting pre-trained knowledge and over-fitting to data-specific ones. To circumvent these issues, we propose a novel Forgery-aware Adaptive Vision Transformer (FA-ViT). In FA-ViT, the vanilla ViT's parameters are frozen to preserve its pre-trained knowledge, while two specially designed components, the Local-aware Forgery Injector (LFI) and the Global-aware Forgery Adaptor (GFA), are employed to adapt forgery-related knowledge. our proposed FA-ViT effectively combines these two different types of knowledge to form the general forgery features for detecting Deepfakes. Specifically, LFI captures local discriminative information and incorporates these information into ViT via Neighborhood-Preserving Cross Attention (NPCA). Simultaneously, GFA learns adaptive knowledge in the self-attention layer, bridging the gap between the two different domain. Furthermore, we design a novel Single Domain Pairwise Learning (SDPL) to facilitate fine-grained information learning in FA-ViT. The extensive experiments demonstrate that our FA-ViT achieves state-of-the-art performance in cross-dataset evaluation and cross-manipulation scenarios, and improves the robustness against unseen perturbations