Intra-variance Guided Metric Learning for Face Forgery Detection

Zhentao Chen, Junlin Hu

Abstract: Since facial manipulation technology has raised serious concerns, facial forgery detection has also attracted increasing attention. Although recent work has made good achievements, the detection of unseen fake faces is still a big challenge. In this paper, we tackle facial forgery detection problem from the perspective of distance metric learning, and design a new Intra-Variance guided Metric Learning (IVML) method to drive classification and adopt Vision Transformer (ViT) as the backbone, which aims to improve the generalization ability of face forgery detection methods. Specifically, considering that there is a large gap between different real faces, our proposed IVML method increases the distance between real and fake faces while maintaining a certain distance within real faces. We choose ViT as the backbone as our experiments prove that ViT has better generalization ability in face forgery detection. A large number of experiments demonstrate the effectiveness and superiority of our IVML method in cross-dataset evaluation.