Diffusion models have shown remarkable success in visual synthesis, but have also raised
concerns about potential abuse for malicious purposes. In this paper, we seek to build a …

Over the past decade, there has been tremendous progress in creating synthetic media,
mainly thanks to the development of powerful methods based on generative adversarial …

The aim of this work is to explore the potential of pre-trained vision-language models (VLMs)
for universal detection of AI-generated images. We develop a lightweight detection strategy …

Recently the proliferation of highly realistic synthetic images facilitated through a variety of
GANs and Diffusions has significantly heightened the susceptibility to misuse. While the …

Diffusion models (DMs) have recently emerged as a promising method in image synthesis.
However, to date, only little attention has been paid to the detection of DM-generated …

Recent advancements in diffusion models have enabled the generation of realistic
deepfakes from textual prompts in natural language. While these models have numerous …

Detecting AI-generated images has become an extraordinarily difficult challenge as new
generative architectures emerge on a daily basis with more and more capabilities and …

AI-generated synthetic media, also called Deepfakes, have significantly influenced so many
domains, from entertainment to cybersecurity. Generative Adversarial Networks (GANs) and …

Recent progress in visual generative models enables the generation of high-quality images.
To prevent the misuse of generated images, it is important to identify the origin model that …

With the rapid development of deep generative models (such as Generative Adversarial
Networks and Diffusion models), AI-synthesized images are now of such high quality that …