Beyond Seeing Is Believing: On Crowdsourced Detection of Audiovisual Deepfakes

ArXi:2605.04797v1 Announce Type: cross Deepfakes are increasingly realistic and easy to produce, raising concerns about the reliability of human judgments in misinformation settings. We study audiovisual deepfake detection by measuring how consistently crowd workers distinguish authentic from manipulated videos and, when they flag a video as manipulated, how accurately they identify the manipulation type (audio-only, video-only, or audio-video) and how consistently they report manipulation.