Deepfake Detection With and Without Content Warnings

Andrew Lewis, Patrick Vu, Raymond Duch and Areeq Chowdhury
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Raymond Duch: University of Oxford

No cb7rw, OSF Preprints from Center for Open Science

Abstract: The rapid advancement of ‘deepfake’ video technology — which uses deep learning artificial intelligence algorithms to create fake videos that look real — has given urgency to the question of how policymakers and technology companies should moderate inauthentic content. We conduct an experiment to measure people’s alertness to and ability to detect a high-quality deepfake amongst a set of videos. First, we find that in a natural setting with no content warnings, individuals who are exposed to a deepfake video of neutral content are no more likely to detect anything out of the ordinary (32.9%) compared to a control group who viewed only authentic videos (34.1%). Second, we find that when individuals are given a warning that at least one video in a set of five videos is a deepfake, only 21.6% of respondents correctly identify the deepfake as the only inauthentic video, while the remainder erroneously select at least one genuine video as a deepfake.

Date: 2023-10-15
New Economics Papers: this item is included in nep-ain, nep-big and nep-exp
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:cb7rw

DOI: 10.31219/osf.io/cb7rw

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