Warning: Humans Cannot Reliably Detect Speech Deepfakes

Authors: Kimberly T. Mai, Sergi D. Bray, Toby Davies, Lewis D. Griffin

Published: 2023-01-19 00:17:48+00:00

AI Summary

This study investigates human capabilities in detecting speech deepfakes through an online experiment involving 529 participants listening to English and Mandarin audio clips. The results show that human detection accuracy is unreliable, reaching only 73% accuracy, with no significant difference between languages; brief familiarization with deepfake examples only marginally improves performance.

Abstract

Speech deepfakes are artificial voices generated by machine learning models. Previous literature has highlighted deepfakes as one of the biggest security threats arising from progress in artificial intelligence due to their potential for misuse. However, studies investigating human detection capabilities are limited. We presented genuine and deepfake audio to n = 529 individuals and asked them to identify the deepfakes. We ran our experiments in English and Mandarin to understand if language affects detection performance and decision-making rationale. We found that detection capability is unreliable. Listeners only correctly spotted the deepfakes 73% of the time, and there was no difference in detectability between the two languages. Increasing listener awareness by providing examples of speech deepfakes only improves results slightly. As speech synthesis algorithms improve and become more realistic, we can expect the detection task to become harder. The difficulty of detecting speech deepfakes confirms their potential for misuse and signals that defenses against this threat are needed.