文章基本信息

标题：Fooled twice: People cannot detect deepfakes but think they can
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作者：Nils C. Köbis ; Barbora Doležalová ; Ivan Soraperra 等
期刊名称：iScience
印刷版ISSN：2589-0042
出版年度：2021
卷号：24
期号：11
页码：1-18
DOI：10.1016/j.isci.2021.103364
语种：English
出版社：Elsevier
摘要：SummaryHyper-realistic manipulations of audio-visual content, i.e., deepfakes, present new challenges for establishing the veracity of online content. Research on the human impact of deepfakes remains sparse. In a pre-registered behavioral experiment (N = 210), we show that (1) people cannot reliably detect deepfakes and (2) neither raising awareness nor introducing financial incentives improves their detection accuracy. Zeroing in on the underlying cognitive processes, we find that (3) people are biased toward mistaking deepfakes as authentic videos (rather than vice versa) and (4) they overestimate their own detection abilities. Together, these results suggest that people adopt a “seeing-is-believing” heuristic for deepfake detection while being overconfident in their (low) detection abilities. The combination renders people particularly susceptible to be influenced by deepfake content.Graphical abstractDisplay OmittedHighlights•People cannot reliably detect deepfakes•Raising awareness and financial incentives do not improve people's detection accuracy•People tend to mistake deepfakes as authentic videos (rather than vice versa)•People overestimate their own detection deepfake abilitiesNeuroscience; Behavioral neuroscience; Cognitive neuroscience; Artificial intelligence; Artificial intelligence applications; Social sciences; Psychology