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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Deepfake detection with and without content warnings.

Andrew Lewis1, Patrick Vu2, Raymond M Duch1

  • 1University of Oxford, Oxford, UK.

Royal Society Open Science
|November 29, 2023
PubMed
Summary
This summary is machine-generated.

Deepfake detection is challenging, even with warnings. People struggle to identify AI-generated fake videos, indicating a need for better moderation strategies for inauthentic content.

Keywords:
deepfakeexperimentsmanual detection

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Area of Science:

  • Computer Science
  • Media Studies
  • Psychology

Background:

  • Deepfake technology, utilizing deep learning AI, creates realistic fake videos, posing challenges for content moderation.
  • The proliferation of inauthentic content necessitates understanding public perception and detection capabilities.

Purpose of the Study:

  • To experimentally measure individuals' alertness and accuracy in detecting high-quality deepfake videos.
  • To assess the impact of content warnings on deepfake identification.

Main Methods:

  • An experiment was conducted exposing participants to both authentic and deepfake videos.
  • Two conditions were tested: natural exposure without warnings and exposure with a warning about the presence of a deepfake.

Main Results:

  • Without warnings, participants exposed to deepfakes showed no significant difference in detecting anomalies (32.9%) compared to a control group (34.1%).
  • With a warning, only 21.6% correctly identified the single deepfake, with others misclassifying genuine videos as fake.

Conclusions:

  • Individuals exhibit low baseline awareness of deepfakes in natural settings.
  • Content warnings do not reliably improve deepfake detection and may even lead to misidentification of authentic content.