Deepfake Detection for Fake Images with Facemasks
Sangjun Lee, Donggeun Ko, Jin-Yong Park, Saebyeol Shin, Dong-Hee Hong, Simon S. Woo · 2022
Hyper-realistic face image generation and manipulation have given rise to numerous unethical social issues, e.g., invasion of privacy, threat of security, and malicious political maneuvering, which resulted 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 the COVID-19. In this paper, we thoroughly evaluate the performance of state-of-the-art deepfake detection models on the deepfakes with the facemask. Our result shows that fake facial images with facemask can deceive well-known deepfake detection models, thereby evading the real-world security systems.