Deepfake videos are becoming increasingly realistic, showing few tampering traces on facial areasthat vary between frames. Consequently, 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, leading to 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，其面部区域几乎不残留帧间可变的篡改痕迹。因此，现有深度伪造检测方法难以在准确定位篡改区域的同时，识别未知领域的深度伪造视频。为解决这一局限，我们提出Delocate——一种既能识别又能定位未知领域深度伪造视频的新型检测模型。该方法包含两个阶段：重建阶段与定位阶段。在重建阶段，模型随机遮蔽感兴趣区域（ROIs），并重构无篡改痕迹的真实人脸，从而对真实人脸获得相对良好的重建效果，而对伪造人脸则产生较差的重建效果。在定位阶段，利用重建阶段的输出与伪造真值掩膜作为监督信号，引导伪造定位过程。该策略着重强化重建效果较差的伪造人脸的重建阶段，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的大量实验表明，Delocate不仅在篡改区域定位上表现优异，还能有效提升跨域检测性能。