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Deepfake Detection With and Without Content Warnings

Author

Listed:
  • Lewis, Andrew
  • Vu, Patrick
  • Duch, Raymond

    (University of Oxford)

  • Chowdhury, Areeq

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.

Suggested Citation

  • Lewis, Andrew & Vu, Patrick & Duch, Raymond & Chowdhury, Areeq, 2023. "Deepfake Detection With and Without Content Warnings," OSF Preprints cb7rw, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:cb7rw
    DOI: 10.31219/osf.io/cb7rw
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    References listed on IDEAS

    as
    1. Nathan F. Dieckmann & Robin Gregory & Ellen Peters & Robert Hartman, 2017. "Seeing What You Want to See: How Imprecise Uncertainty Ranges Enhance Motivated Reasoning," Risk Analysis, John Wiley & Sons, vol. 37(3), pages 471-486, March.
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