用双监督提升DINOv2对不同伪造手法的识别能力
Fine-Grained DINO Tuning with Dual Supervision for Face Forgery Detection
- 在DINOv2中加入轻量多头LoRA模块，实现高效微调
- 同时完成真伪判断与伪造类型分类，准确率媲美甚至超越复杂模型
- 仅350万参数即可达到高精度，适合资源受限场景
深度伪造技术的泛滥严重威胁信息真实性。尽管DINOv2在检测中展现潜力，现有微调方法将其视为通用二分类任务，忽略了不同伪造方法特有的痕迹。为此，我们提出针对DINOv2的细粒度伪造适配器（DFF-Adapter）。该方法在每个Transformer模块中引入轻量级多头LoRA模块，实现高效骨干网络适配。DFF-Adapter同时处理真实性检测与细粒度篡改类型分类，通过分类伪造手法提升对伪造痕迹的敏感性。我们设计共享分支，将细粒度篡改线索传递至真实性检测头，实现多任务协同优化，显式增强真伪判别能力。仅使用350万可训练参数，本方法达到与当前复杂先进方法相当或更优的检测准确率。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。