In recent years, face synthesis and manipulation technology have been developed rapidly, and now it is feasible to synthesize extremely realistic fake face videos, which can easily deceive existing face recognition systems. Due to the high quality of fake videos, allowing fake face videos to propagate on Internet may cause serious ethical, moral and legal problems. Therefore, the effective and reliable detection method is urgently needed to distinguish fake face videos. We notice that existing face forgery methods commonly extract face area of each frame first and perform manipulations only on face areas while background areas remain unchanged. Therefore, the difference between the face area and the background area in a forged face frame is significantly larger than the difference between the face area and the background area in the corresponding unforged frame. In this paper, based on such observation, we propose a new detection method—forensic similarity method—which judges the authenticity of face video frames by detecting the difference in similarity between the face area and the background area. For evaluation, we conduct training and testing on FaceForensics++ dataset, and evaluate the generalization capability on Celeb-DF dataset. From the experimental results, we can find that the proposed method has a better or comparable performance, especially in the term of generalization capability. Compared with Xception, our model can attain 8–12% accuracy gains under Celeb-DF dataset. In addition, our model has lower complexity than Xception.

近年来，人脸合成和操纵技术得到了迅速发展，现在合成非常逼真的假人脸视频是可行的，它可以轻易地欺骗现有的人脸识别系统。由于假视频的质量很高，允许假脸视频在Internet上传播可能会导致严重的道德，道德和法律问题。因此，迫切需要一种有效可靠的检测方法来区分假人脸视频。我们注意到，现有的面部伪造方法通常首先提取每个帧的面部区域，并且仅在面部区域上执行操作，而背景区域保持不变。所以，伪造的面部框架中的面部区域和背景区域之间的差异明显大于相应的非伪造框架中的面部区域和背景区域之间的差异。在这种观察的基础上，我们提出了一种新的检测方法-取证相似性方法-通过检测面部区域与背景区域之间的相似度差异来判断面部视频帧的真实性。为了进行评估，我们对FaceForensics ++数据集进行了培训和测试，并评估了Celeb-DF数据集的泛化能力。从实验结果中，我们发现所提出的方法具有更好或相当的性能，特别是在泛化能力方面。与Xception相比，我们的模型在Celeb-DF数据集下可以获得8-12％的准确度。此外，