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 …

Synthetic media—so-called deep fakes—have captured the imagination of some and struck
fear in others. Although they vary in their form and creation, deep fakes refer to text, image …

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 …

Multimedia data manipulation and forgery has never been easier than today, thanks to the
power of Artificial Intelligence (AI). AI-generated fake content, commonly called Deepfakes …

Generative adversarial networks (GANs) have been used to create remarkably realistic
images of people. More recently, diffusion-based techniques have taken image synthesis to …

Deepfakes present an emerging threat in cyberspace. Recent developments in machine
learning make deepfakes highly believable, and very difficult to differentiate between what is …

In the last few years, we have witnessed the rise of a series of deep learning methods to
generate synthetic images that look extremely realistic. These techniques prove useful in the …

The ever higher quality and wide diffusion of fake images have spawn a quest for reliable
forensic tools. Many GAN image detectors have been proposed, recently. In real world …

Advances in face synthesis have raised alarms about the deceptive use of synthetic faces.
Can synthetic identities be effectively used to fool human observers? In this paper, we …

Abstract Modern Generative Adversarial Networks (GANs) generate realistic images
remarkably well. Previous work has demonstrated the feasibility of" GAN-classifiers" that are …