Identity-Agnostic Learning for Deepfake Face Detection

Abstract

Despite the promising results obtained by existing deepfake face detection methods for within-dataset detection, they often fail to generalize effectively to new datasets. We hypothesize that identity, a significant feature in facial recognition, is a key factor affecting deepfake detection models’ cross-dataset performance. In the feature space learned by a real/fake classifier, facial features may cluster based on identity rather than their authenticity, which undermines the classifier’s ability to distinguish between real and fake images. This paper introduces a novel training approach called Identity-Agnostic Learning (IAL) for deepfake face detection. IAL trains the detection model with identity-agnostic manner. It thus guides model to pay attention to the identity-irrelevant features. Experimental results demonstrate that our method effectively enhances the overall generalizability of deepfake face detection models.