On the Realness of People Who Do Not Exist: The Social Processing of Artificial Faces

Abstract

Today more than ever, we are asked to judge the realness, truthfulness and trustworthiness of our social world. We here focus on how people perceive artificially-generated faces. Faces generated by Generative adversarial networks (GANs) are realistic-looking faces of non-existing people, increasingly used in marketing, journalism, social media, and political propaganda. Across three studies, we investigated if and how participants can distinguish between GAN and REAL faces and the social consequences of exposure to artificial faces. GAN faces were more likely perceived as real than REAL faces, a pattern partly explained by intrinsic stimuli characteristics. Moreover, participants’ realness judgments influenced their behaviour, displaying increased social conformity towards faces perceived as real , independently of their actual realness. Lastly, knowledge about the presence of GAN faces eroded social trust. Our findings point to potentially far-reaching consequences of the pervasive use of GAN faces in a culture powered by images at unprecedented levels.