Relation Conflict Estimation for Generalizable Face Forgery Detection

Generalizable face forgery detection remains challenging because models trained on known manipulations often overfit to specific appearance artifacts, which may not transfer to unseen forgery mechanisms. However, the discriminative evidence carried by facial relation conflicts remains insufficiently explored. We observe that authentic facial videos tend to preserve stronger compatibility between facial patch representations and their surrounding contexts, whereas forgery synthesis may introduce subtle contextual discrepancies even when local appearances remain visually plausible. Motivated by this observation, we propose a Relation Conflict Estimation framework for generalizable face forgery detection, which exploits contextual discrepancies among facial regions as transferable forgery evidence. Lightweight distributional guidance is further introduced to regularize relation representations in terms of frame-level conflict magnitude and cross-frame distribution. Extensive experiments under cross-dataset, cross-manipulation, and cross-generation protocols demonstrate that the proposed method consistently improves generalization across both manipulation-based and AIGC-generated forgeries, with consistent gains observed on ViT-B/16 and CLIP ViT-L/14 backbones. Ablation and visualization analyses further show that pairwise relation-conflict separation provides the primary regularization effect, while cross-frame distribution guidance offers complementary improvements.