Exploring Self-Supervised Vision Transformers for Deepfake Detection: A Comparative Analysis

arXiv:2405.00355v1 Announce Type: new
Abstract: This paper investigates the effectiveness of self-supervised pre-trained transformers compared to supervised pre-trained transformers and conventional neural networks (ConvNets) for detecting various types of deepfakes. We focus on their potential for improved generalization, particularly when training data is limited. Despite the notable success of large vision-language models utilizing transformer architectures in various tasks, including zero-shot and few-shot learning, the deepfake detection community has still shown some reluctance to adopt pre-trained vision transformers (ViTs), especially large …

abstract analysis arxiv comparative analysis cs.cv data deepfake deepfakes detection focus networks neural networks paper success training training data transformers type types vision vision transformers