Testing human ability to detect ‘deepfake’ images of human faces

Abstract

‘Deepfakes’ are computationally created entities that falsely represent reality. They can take image, video, and audio modalities, and pose a threat to many areas of systems and societies, comprising a topic of interest to various aspects of cybersecurity and cybersafety. In 2020, a workshop consulting AI experts from academia, policing, government, the private sector, and state security agencies ranked deepfakes as the most serious AI threat. These experts noted that since fake material can propagate through many uncontrolled routes, changes in citizen behaviour may be the only effective defence. This study aims to assess human ability to identify image deepfakes of human faces (these being uncurated output from the StyleGAN2 algorithm as trained on the FFHQ dataset) from a pool of non-deepfake images (these being random selection of images from the FFHQ dataset), and to assess the effectiveness of some simple interventions intended to improve detection accuracy. Using an online survey, participants (N = 280) were randomly allocated to one of four groups: a control group, and three assistance interventions. Each participant was shown a sequence of 20 images randomly selected from a pool of 50 deepfake images of human faces and 50 images of real human faces. Participants were asked whether each image was AI-generated or not, to report their confidence, and to describe the reasoning behind each response. Overall detection accuracy was only just above chance and none of the interventions significantly improved this. Of equal concern was the fact that participants’ confidence in their answers was high and unrelated to accuracy. Assessing the results on a per-image basis reveals that participants consistently found certain images easy to label correctly and certain images difficult, but reported similarly high confidence regardless of the image. Thus, although participant accuracy was 62% overall, this accuracy across images ranged quite evenly between 85 and 30%, with an accuracy of below 50% for one in every five images. We interpret the findings as suggesting that there is a need for an urgent call to action to address this threat.

AI-generated faces show lower morphological diversity than real faces do

“…Moreover, AI-synthetized faces are less variable in facial shape, i.e., show lower morphological disparity, and have lower levels of facial asymmetry that natural faces do. From the perspective of objectively quantifiable morphometric measurements, artificial and natural faces are still distinguishable, although people cannot see these differences (Bray et al 2023;Lago et al 2022;Nightingale and Farid 2022;Rossi et al 2022;Tucciarelli et al 2022).…”

“…In this study, we wanted to investigate whether the mean morphometric features -including symmetry and shape variance, here measured as morphological disparity -of AI-generated faces are the same as those of natural faces. Unlike several previous studies (Bray, Johnson, and Kleinberg 2023;Lago et al 2022;Nightingale and Farid 2022;Rossi et al 2022;Tucciarelli et al 2022), we used standardized synthetized faces with a neutral expression and compared them to natural faces selected from our database of standardized facial portraits. Recent studies have shown that humans are no longer able to distinguish artificially generated facial stimuli from portrait photographs of real human beings.…”

Local Government Cybersecurity Landscape: A Systematic Review and Conceptual Framework

“…While the existing literature provides insightful discussions on many types and techniques of cyber-attacks, it notably overlooks emerging threats, which could be vital for local governments. Emerging threats in Internet security include deepfake technology and AI-powered social engineering attacks, which can lead to misinformation or the manipulation of public opinion [92,93]. In web security, newer threats such as API attacks [94] and crypto-jacking that target web-based applications are often overlooked [95,96].…”

Embracing deepfakes and AI‐generated images in neuroscience research

“…This prompts the question—is AI‐generated content ecologically valid? Behavioural research confirms that it is difficult for humans to detect high‐quality AI‐generated images, suggesting they accurately portray real people, places and things (e.g., Bray et al, 2022; Korshunov & Marcel, 2020; Lu et al, 2023; Shen et al, 2021). The ultimate test might be whether real and AI‐generated faces elicit similar neural responses, given human expertise in face perception (Haxby et al, 2000).…”