Fooled twice: People cannot detect deepfakes but think they can

Summary: Hyper-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 relia...

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Autores principales:	Nils C. Köbis, Barbora Doležalová, Ivan Soraperra
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Neuroscience Behavioral neuroscience Cognitive neuroscience Artificial intelligence Artificial intelligence applications Social sciences Science Q
Acceso en línea:	https://doaj.org/article/92ef429a157a43d99c880545b95ab391
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id	oai:doaj.org-article:92ef429a157a43d99c880545b95ab391
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spelling	oai:doaj.org-article:92ef429a157a43d99c880545b95ab3912021-11-20T05:10:42ZFooled twice: People cannot detect deepfakes but think they can2589-004210.1016/j.isci.2021.103364https://doaj.org/article/92ef429a157a43d99c880545b95ab3912021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2589004221013353https://doaj.org/toc/2589-0042Summary: Hyper-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.Nils C. KöbisBarbora DoležalováIvan SoraperraElsevierarticleNeuroscienceBehavioral neuroscienceCognitive neuroscienceArtificial intelligenceArtificial intelligence applicationsSocial sciencesScienceQENiScience, Vol 24, Iss 11, Pp 103364- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Neuroscience Behavioral neuroscience Cognitive neuroscience Artificial intelligence Artificial intelligence applications Social sciences Science Q
spellingShingle	Neuroscience Behavioral neuroscience Cognitive neuroscience Artificial intelligence Artificial intelligence applications Social sciences Science Q Nils C. Köbis Barbora Doležalová Ivan Soraperra Fooled twice: People cannot detect deepfakes but think they can
description	Summary: Hyper-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.
format	article
author	Nils C. Köbis Barbora Doležalová Ivan Soraperra
author_facet	Nils C. Köbis Barbora Doležalová Ivan Soraperra
author_sort	Nils C. Köbis
title	Fooled twice: People cannot detect deepfakes but think they can
title_short	Fooled twice: People cannot detect deepfakes but think they can
title_full	Fooled twice: People cannot detect deepfakes but think they can
title_fullStr	Fooled twice: People cannot detect deepfakes but think they can
title_full_unstemmed	Fooled twice: People cannot detect deepfakes but think they can
title_sort	fooled twice: people cannot detect deepfakes but think they can
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/92ef429a157a43d99c880545b95ab391
work_keys_str_mv	AT nilsckobis fooledtwicepeoplecannotdetectdeepfakesbutthinktheycan AT barboradolezalova fooledtwicepeoplecannotdetectdeepfakesbutthinktheycan AT ivansoraperra fooledtwicepeoplecannotdetectdeepfakesbutthinktheycan
_version_	1718419555563339776

Fooled twice: People cannot detect deepfakes but think they can

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