This work is an update of my previous paper on the same topic published a few years ago
(Borji, 2019). With the dramatic progress in generative modeling, a suite of new quantitative …

Abstract Generative Adversarial Networks (GAN) have led to the generation of very realistic
face images, which have been used in fake social media accounts and other disinformation …

Generative image modeling enables a wide range of applications but raises ethical
concerns about responsible deployment. This paper introduces an active strategy combining …

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 …

Detecting fake images is becoming a major goal of computer vision. This need is becoming
more and more pressing with the continuous improvement of synthesis methods based on …

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 …

DeepFakes which refer to AI-generated media content have become an increasing concern
due to their use as a means for disinformation. Detecting DeepFakes is currently solved with …

In the course of the past few years, diffusion models (DMs) have reached an unprecedented
level of visual quality. However, relatively little attention has been paid to the detection of DM …

With recent text-to-image models anyone can generate deceptively realistic images with
arbitrary contents fueling the growing threat of visual disinformation. A key enabler for …

The diversity of human faces and the contexts in which they appear gives rise to an
expansive stimulus space over which people infer psychological traits (eg, trustworthiness or …