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Fooled twice: People cannot detect deepfakes but think they can

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 detec...

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Detalles Bibliográficos
Autores principales: Köbis, Nils C., Doležalová, Barbora, Soraperra, Ivan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8602050/
https://www.ncbi.nlm.nih.gov/pubmed/34820608
http://dx.doi.org/10.1016/j.isci.2021.103364
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author Köbis, Nils C.
Doležalová, Barbora
Soraperra, Ivan
author_facet Köbis, Nils C.
Doležalová, Barbora
Soraperra, Ivan
author_sort Köbis, Nils C.
collection PubMed
description 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.
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spelling pubmed-86020502021-11-23 Fooled twice: People cannot detect deepfakes but think they can Köbis, Nils C. Doležalová, Barbora Soraperra, Ivan iScience Article 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. Elsevier 2021-10-29 /pmc/articles/PMC8602050/ /pubmed/34820608 http://dx.doi.org/10.1016/j.isci.2021.103364 Text en © 2021 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Köbis, Nils C.
Doležalová, Barbora
Soraperra, Ivan
Fooled twice: People cannot detect deepfakes but think they can
title 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_short Fooled twice: People cannot detect deepfakes but think they can
title_sort fooled twice: people cannot detect deepfakes but think they can
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8602050/
https://www.ncbi.nlm.nih.gov/pubmed/34820608
http://dx.doi.org/10.1016/j.isci.2021.103364
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