Abstract
BackgroundRecent experimental work has shown that hyper-realistic face masks can pass for real faces during live viewing. However, live viewing embeds the perceptual task (mask detection) in a powerful social context that may influence respondents’ behaviour. To remove this social context, we assessed viewers’ ability to distinguish photos of hyper-realistic masks from photos of real faces in a computerised two-alternative forced choice (2AFC) procedure.ResultsIn experiment 1 (N = 120), we observed an error rate of 33% when viewing time was restricted to 500 ms. In experiment 2 (N = 120), we observed an error rate of 20% when viewing time was unlimited. In both experiments we saw a significant performance cost for other-race comparisons relative to own-race comparisons.ConclusionsWe conclude that viewers could not reliably distinguish hyper-realistic face masks from real faces in photographic presentations. As well as its theoretical interest, failure to detect synthetic faces has important implications for security and crime prevention, which often rely on facial appearance and personal identity being related.
Highlights
Recent experimental work has shown that hyper-realistic face masks can pass for real faces during live viewing
How are we to judge the success of such imitations? In 1950, Alan Turing proposed an influential answer for the specific case of artificial intelligence: an imitation is successful when we cannot distinguish it from the real thing (Turing, 1950)
Simple main effects confirmed that there was a significant effect of mask type for both own-race (F (1,118 = 76.96, p < .001, partial η2 = 0.40, Cohen’s d = 1.63) and other-race faces (F (1,118 = 131.26, p < .001, partial η2 = 0.53, Cohen’s d = 2.12)
Summary
Recent experimental work has shown that hyper-realistic face masks can pass for real faces during live viewing. Live viewing embeds the perceptual task (mask detection) in a powerful social context that may influence respondents’ behaviour To remove this social context, we assessed viewers’ ability to distinguish photos of hyper-realistic masks from photos of real faces in a computerised two-alternative forced choice (2AFC) procedure. In 1950, Alan Turing proposed an influential answer for the specific case of artificial intelligence: an imitation is successful when we cannot distinguish it from the real thing (Turing, 1950). In his original argument, Turing imagined a human evaluator engaged in natural language conversations with a real human and a computer designed to generate human-like responses. If the Sanders et al Cognitive Research: Principles and Implications (2019) 4:43 evaluator cannot reliably distinguish the computer from the human, the computer is said to pass the test
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