Deepfake detection by human crowds, machines, and machine-informed crowds


Courtesy of the researchers

Courtesy of the researchers

Matthew Groh, Ziv Epstein, Chaz Firestone, Rosalind Picard. "Deepfake detection by human crowds, machines, and machine-informed crowds." Proceedings of the National Academy of Sciences Jan 2022, 119 (1) e2110013119; DOI: 10.1073/pnas.2110013119


The recent emergence of machine-manipulated media raises an important societal question: How can we know whether a video that we watch is real or fake? In two online studies with 15,016 participants, we present authentic videos and deepfakes and ask participants to identify which is which. We compare the performance of ordinary human observers with the leading computer vision deepfake detection model and find them similarly accurate, while making different kinds of mistakes. Together, participants with access to the model’s prediction are more accurate than either alone, but inaccurate model predictions often decrease participants’ accuracy. To probe the relative strengths and weaknesses of humans and machines as detectors of deepfakes, we examine human and machine performance across video-level features, and we evaluate the impact of preregistered randomized interventions on deepfake detection. We find that manipulations designed to disrupt visual processing of faces hinder human participants’ performance while mostly not affecting the model’s performance, suggesting a role for specialized cognitive capacities in explaining human deepfake detection performance.

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