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The recent emergence of deepfake videos raises theoretical and practical questions. Are humans or the leading machine learning model more capable of detecting algorithmic visual manipulations of videos? How should content moderation systems be designed to detect and flag video-based misinformation? We present data showing that ordinary humans perform in the range of the leading machine learning model on a large set of minimal context videos. While we find that a system integrating human and model predictions is more accurate than either humans or the model alone, we show inaccurate model predictions often lead humans to incorrectly update their responses. Finally, we demonstrate that specialized face processing and the ability to consider context may specially equip humans for deepfake detection. The datasets and code generated and analyzed during the current study are available in our public GitHub repository, <https://github.com/mattgroh/cognitive-science-detecting-deepfakes>.

Alicia guoKim jong unVladimir putinMassachusetts institute of technology committeeMedia labDeepfake detection challengeDetect fakesTwo alternative forced choiceSingle video designKim jung unMassachusetts instituteExperimental subjectsExempt categoryBenign behavioralDeep fakesAffective computing