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Warning: Humans cannot reliably detect speech deepfakes.

Kimberly T Mai1,2, Sergi Bray1,2, Toby Davies1

  • 1Department of Security and Crime Science, University College London, London, United Kingdom.

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Summary
This summary is machine-generated.

Human ability to detect artificial speech deepfakes is unreliable, with listeners correctly identifying them only 73% of the time. This highlights the urgent need for advanced defenses against AI-generated voice threats.

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Area of Science:

  • Artificial Intelligence
  • Human-Computer Interaction
  • Cybersecurity

Background:

  • Speech deepfakes, AI-generated artificial voices, pose significant security risks due to potential misuse.
  • Limited research exists on human capabilities to detect these sophisticated audio forgeries.

Purpose of the Study:

  • To evaluate human accuracy in identifying speech deepfakes.
  • To investigate the influence of language (English vs. Mandarin) on deepfake detection performance and rationale.
  • To assess the impact of listener awareness on detection capabilities.

Main Methods:

  • Presented genuine and deepfake audio samples to 529 participants.
  • Conducted experiments in both English and Mandarin languages.
  • Analyzed detection accuracy and decision-making processes.

Main Results:

  • Human detection of speech deepfakes was unreliable, with an average accuracy of 73%.
  • No significant difference in detectability was observed between English and Mandarin speakers.
  • Providing examples of deepfakes resulted in only marginal improvements in detection accuracy.

Conclusions:

  • Current human capabilities are insufficient to reliably detect advanced speech deepfakes.
  • The increasing realism of speech synthesis necessitates the development of robust technological defenses.
  • Speech deepfakes represent a growing cybersecurity threat requiring proactive mitigation strategies.