Deepfake Detection for Facial Images with Facemasks

• URL: http://arxiv.org/abs/2202.11359v1
• Date: Wed, 23 Feb 2022 09:01:27 GMT
• Title: Deepfake Detection for Facial Images with Facemasks
• Authors: Donggeun Ko, Sangjun Lee, Jinyong Park, Saebyeol Shin, Donghee Hong, Simon S. Woo
• Abstract summary: We thoroughly evaluate the performance of state-of-the-art deepfake detection models on the deepfakes withthe facemask. We propose two approaches to enhance themasked deepfakes detection:face-patchandface-crop.
• Score: 17.238556058316412
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 methodswith the rising demands of deepfake forensics. Proposed deepfakedetection methods to date have shown remarkable detection perfor-mance and robustness. However, none of the suggested deepfakedetection methods assessed the performance of deepfakes withthe facemask during the pandemic crisis after the outbreak of theCovid-19. In this paper, we thoroughly evaluate the performance ofstate-of-the-art deepfake detection models on the deepfakes withthe facemask. Also, we propose two approaches to enhance themasked deepfakes detection:face-patchandface-crop. The experi-mental 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-cropperforms better than theface-patch, and could be a trainmethod for deepfake detection models to detect fake faces withfacemask in real world.

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