CLIPping the Deception: Adapting Vision-Language Models for Universal Deepfake Detection

arXiv:2402.12927v1 Announce Type: new
Abstract: The recent advancements in Generative Adversarial Networks (GANs) and the emergence of Diffusion models have significantly streamlined the production of highly realistic and widely accessible synthetic content. As a result, there is a pressing need for effective general purpose detection mechanisms to mitigate the potential risks posed by deepfakes. In this paper, we explore the effectiveness of pre-trained vision-language models (VLMs) when paired with recent adaptation methods for universal deepfake detection. Following previous studies in …

abstract adversarial arxiv cs.cv deception deepfake detection diffusion diffusion models emergence gans general generative generative adversarial networks language language models networks production synthetic type vision vision-language models