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，在人脸区域呈现出帧间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检测未知领域的深度伪造视频，同时准确定位篡改区域。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：恢复与定位。在恢复阶段，模型随机遮蔽感兴趣区域（ROI），并重建未经篡改的真实人脸，使得真实人脸获得较优的恢复效果，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。该策略策略性地强化了恢复效果较差的伪造人脸的恢复过程，从而促进篡改区域的定位。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方面表现出色，还提升了跨域检测性能。