Low-rank Orthogonal Subspace Intervention for Generalizable Face Forgery Detection

ArXi:2601.11915v2 Announce Type: replace The generalization problem remains a key challenge in face forgery detection. This paper explores the reasons for the generalization failure of Vanilla CLIP: in ``real vs. fake" detection, the few dominant principal components in the feature space primarily encode forgery-irrelevant information, rather than authentic forgery traces. However, this irrelevant information inevitably leads to spurious correlations, severely limiting detector performance. We define this phenomenon as ``low-rank spurious bias