Previous face forgery detection methods mainly focus on appearance features, which may be easily attacked by sophisticated manipulation. Considering the majority of current face manipulation methods generate fake faces based on a single frame, which do not take frame consistency and coordination into consideration, artifacts on frame sequences are more effective for face forgery detection. However, current sequence-based face forgery detection methods use general video classification networks directly, which discard the special and discriminative motion information for face manipulation detection. To this end, we propose an effective sequence-based forgery detection framework based on an existing video classification method. To make the motion features more expressive for manipulation detection, we propose an alternative motion consistency block instead of the original motion features module. To make the learned features more generalizable, we propose an auxiliary anomaly detection block. With these two specially designed improvements, we make a general video classification network achieve promising results on three popular face forgery datasets.

翻译：既往人脸伪造检测方法主要关注外观特征，这类特征易受精细篡改手段的攻击。考虑到当前多数人脸伪造方法基于单帧生成虚假人脸，未考虑帧一致性与协调性，帧序列上的伪影更适用于人脸伪造检测。然而，现有基于帧序列的人脸伪造检测方法直接使用通用视频分类网络，丢弃了针对人脸篡改检测的特殊判别性运动信息。为此，我们基于现有视频分类方法提出了一种有效的序列伪造检测框架。为增强运动特征对篡改检测的表达能力，我们提出替换原始运动特征模块的备选运动一致性模块；为提升学习特征的泛化性，我们引入辅助异常检测模块。通过这两项针对性改进，我们使通用视频分类网络在三个主流人脸伪造数据集上取得了优异性能。