Meta Pseudo Labels Based Deepfake Video Detection
Kyeong-Hwan Moon, Soo-Yul Ok, Jeongil Seo, Suk‐Hwan Lee · Journal of Korea Multimedia Society · 2024
Recently, there has been considerable research on deepfake detection. However, most existing methods face challenges in adapting to the advancements in new generative models within unknown domains. In this paper, our objective is to detect deepfake videos in unknown domains using unlabeled data. Specifically, our proposed approach employs Meta Pseudo Labels (MPL), allowing the model to be trained on unlabeled images. MPL involves the simultaneous training of both a Teacher model and a Student model, where the Teacher model generates Pseudo Labels utilized to train the Student model. This method aims to enhance the adaptability and robustness of deepfake detection systems against emerging unknown domains. The experimental results demonstrate an improvement of 1.91% and 1.87% in ACC and AUROC, respectively, for the known domain. Similarly, in the unknown domain, there is an enhancement of 1.59% in ACC and 1.29% in AUROC.