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

• URL: http://arxiv.org/abs/2511.12107v1
• Date: Sat, 15 Nov 2025 08:57:21 GMT
• Title: Fine-Grained DINO Tuning with Dual Supervision for Face Forgery Detection
• Authors: Tianxiang Zhang, Peipeng Yu, Zhihua Xia, Longchen Dai, Xiaoyu Zhou, Hui Gao,
• Abstract summary: We propose a DeepFake Fine-Grained Adapter (DFF-Adapter) for DINOv2.<n>Our method incorporates lightweight multi-head LoRA modules into every transformer block.<n>Our method achieves detection accuracy comparable to or even surpassing that of current complex state-of-the-art methods.
• Score: 18.62471724010391
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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