Fine-Grained DINO Tuning with Dual Supervision for Face Forgery Detection

The proliferation of sophisticated deepfakes poses significant threats to information integrity.​​ While DINOv2 shows promise for detection, existing fine-tuning approaches treat it as generic binary classification, ​​overlooking distinct artifacts inherent to different deepfake methods.​​ To address this, ​​we propose a DeepFake Fine-Grained Adapter (DFF-Adapter) for DINOv2.​​ Our method incorporates ​​lightweight multi-head LoRA modules​​ into ​​every transformer block​​, enabling efficient backbone adaptation. ​​DFF-Adapter simultaneously addresses authenticity detection and fine-grained manipulation type classification,​​ ​​where classifying forgery methods enhances artifact sensitivity.​​ We introduce ​​a shared branch propagating fine-grained manipulation cues to the authenticity head.​​ ​​This enables multi-task cooperative optimization,​​ explicitly enhancing authenticity discrimination with manipulation-specific knowledge. Utilizing ​​only 3.5M trainable parameters​​, our parameter-efficient approach achieves detection accuracy comparable to or even surpassing that of current complex state-of-the-art methods.