Revisiting Deepfake Detection: BCNet for Robust Generalization Beyond Semantic Dependence

Recent deepfake detection methods leveraging vision foun-dation models (VFMs) like CLIP have made significant progress. How-ever, the abundant semantic information in large training datasets hasmade VFMs highly dependent on semantics. As a result, VFM-basedmethods perform well on images from the same category as the train-ing set but struggle with others. This bias limits generalization in real-world scenarios. To address this issue, we propose the Basis CorrectionNetwork (BCNet), which consists of two modules: the attention-guidedsemantic erasure (ASE), which adaptively identifies and erases seman-tic regions of the image by capturing the model’s semantic attention,and the normalized-gradient perturbation enhancement (NPE) scalesthe gradients of fake samples to concentrated values and adds them asa small perturbation to the original sample, helping the model recog-nize more forgery patterns and improving its ability to distinguish fakefrom real samples. This design ensures the model focuses on the coredistinction between real and fake categories rather than semantic infor-mation. Extensive experiments on 51 AI-generated datasets show thatBCNet achieves 96.7% generalization accuracy on WildRF (collectedfrom real social media) and outperforms other competitors by 8.7% onAIGI-Bench, offering a fresh perspective on semantic generalization indeepfake detection. The code is open-sourced and publicly available athttps://github.com/rstao-bjtu/BCNet.