Hyper-realistic face image generation and manipulation have givenrise to
numerous unethical social issues, e.g., invasion of privacy,threat of security,
and malicious political maneuvering, which re-sulted in the development of
recent deepfake detection methods with the rising demands of deepfake
forensics. Proposed deepfake detection methods to date have shown remarkable
detection performance and robustness. However, none of the suggested deepfake
detection methods assessed the performance of deepfakes with the facemask
during the pandemic crisis after the outbreak of theCovid-19. In this paper, we
thoroughly evaluate the performance of state-of-the-art deepfake detection
models on the deepfakes with the facemask. Also, we propose two approaches to
enhance the masked deepfakes detection: face-patch and face-crop. The
experimental evaluations on both methods are assessed through the base-line
deepfake detection models on the various deepfake datasets. Our extensive
experiments show that, among the two methods, face-crop performs better than
the face-patch, and could be a train method for deepfake detection models to
detect fake faces with facemask in real world.

这篇论文探讨了现代深度学习网络在检测佩戴口罩的 Deepfake 技术方面的性能，并提出了两种不同的检测方法：面部裁剪和面部贴片。实验表明，面部裁剪比面部贴片更为有效，可用于识别现实世界中带口罩的人脸 Deepfake。