Deepfake videos are becoming increasingly realistic, showing subtle tampering traces on facial areasthat vary between frames. Consequently, many existing Deepfake detection methods struggle to detect unknown domain Deepfake videos while accurately locating the tampered region. To address thislimitation, we propose Delocate, a novel Deepfake detection model that can both recognize andlocalize unknown domain Deepfake videos. Ourmethod consists of two stages named recoveringand localization. In the recovering stage, the modelrandomly masks regions of interest (ROIs) and reconstructs real faces without tampering traces, resulting in a relatively good recovery effect for realfaces and a poor recovery effect for fake faces. Inthe localization stage, the output of the recoveryphase and the forgery ground truth mask serve assupervision to guide the forgery localization process. This process strategically emphasizes the recovery phase of fake faces with poor recovery, facilitating the localization of tampered regions. Ourextensive experiments on four widely used benchmark datasets demonstrate that Delocate not onlyexcels in localizing tampered areas but also enhances cross-domain detection performance.

翻译：深度伪造视频日益逼真，其面部区域呈现出跨帧变化的细微篡改痕迹。因此，许多现有深度伪造检测方法难以同时检测未知领域的深度伪造视频并准确定位篡改区域。为克服这一局限，我们提出Delocate——一种新颖的深度伪造检测模型，既能识别也能定位未知领域的深度伪造视频。我们的方法包含两个阶段：恢复阶段与定位阶段。在恢复阶段，模型随机遮蔽感兴趣区域（ROIs），并重建无篡改痕迹的真实人脸，使真实人脸获得相对较好的恢复效果，而伪造人脸恢复效果较差。在定位阶段，恢复阶段的输出与伪造真值掩膜共同作为监督信号，引导伪造定位过程。该过程策略性地强化了对恢复效果较差的伪造人脸的恢复阶段关注，从而促进篡改区域的定位。我们在四个广泛使用的基准数据集上进行了大量实验，结果表明Delocate不仅在篡改区域定位上表现优异，还提升了跨域检测性能。