Today more than ever, we are asked to evaluate the realness, truthfulness and
trustworthiness of our social world. Here, we focus on how people evaluate realistic-looking …

Recent advances in machine learning, specifically generative adversarial networks (GANs),
have made it possible to synthesize highly photo-realistic faces. Such synthetic faces have …

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 …

Generative adversarial network (GAN) generated high-realistic human faces are visually
challenging to discern from real ones. They have been used as profile images for fake social …

Face image synthesis has progressed beyond the point at which humans can effectively
distinguish authentic faces from synthetically-generated ones. Recently developed synthetic …

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

Artificial Intelligence (AI) is increasingly being introduced in several domains for
classification and clustering of different types of existing information (eg, text, images, audio …

Generative adversarial networks (GANs) are able to generate high resolution photo-realistic
images of objects that" do not exist." These synthetic images are rather difficult to detect as …

Generative adversarial networks (GANs) provide powerful architectures for deep generative
learning. GANs have enabled us to achieve an unprecedented degree of realism in the …

Sophisticated generative adversary network (GAN) models are now able to synthesize
highly realistic human faces that are difficult to discern from real ones visually. In this work …