A small team of researchers from The State University of New York at Albany, the State University of New York at Buffalo and Keya Medical has found a common flaw in computer-generated faces by which they can be identified. The group has written a paper describing their findings and have uploaded them to the arXiv preprint server. Over the past couple of years, deepfake pictures and videos have been in the news as amateurs and professional editors alike have created images and videos that depict people doing things that they never actually did. Less reported but related is the increased use of computer-generated images of people that look human but who have never actually existed. Such images are created using generative adversary networks (GANs), and they have reportedly begun showing up on fake social media user profiles, which allows for catfishing and other types of nefarious activity. GANs are a form of deep-learning technology--a neural network is trained on images to learn what human heads and faces look like.

Could this be a computer-generated face? Creating a fake persona online with a computer-generated face is easier than ever before, but there is a simple way to catch these phony pictures – look at the eyes. The inability of artificial intelligence to draw circular pupils gives away whether or not a face comes from a real photograph. Generative adversarial networks (GANs) – a type of AI that can generate images from a simple prompt – can produce realistic-looking faces.