Robust Deepfake Detection via Perturbation Domain Alignment

Abstract

Deepfake detection becomes vital in distinguishing the real image/videos from the fake ones, which are produced via advanced deep learning based face manipulation techniques. Although existing approaches exhibit decent generalization, they struggle to maintain good robustness against diverse perturbations in practical scenarios. Perturbations, which can induce distortions on the original image/video, such as compression, Gaussian noise, blur, etc., tend to introduce negative impacts on the performance of deepfake detection models. Therefore, in this paper, we propose a novel deepfake detection method, named Robust Deepfake Detection via Perturbation Domain Alignment (PDA-RDD), by exploiting the mechanism of domain alignment. Our approach consider different perturbations as distinct domains, and proposes a paired instance momentum whitening (PIMW) module to align these domains, to effectively remove the sensitive information associated with these perturbations. To further enhance PIMW, we construct an MLP projector (MLPP) to project the encoded feature into a more optimal latent vector. Extensive experiments demonstrate the effectiveness of our method on multiple widely used datasets.