Beyond Seeing Is Believing: On Crowdsourced Detection of Audiovisual Deepfakes
A new research paper explores the effectiveness of crowdsourcing for detecting audiovisual deepfakes. The study found that while crowd workers are generally good at identifying authentic videos, they frequently miss manipulated content and struggle to accurately pinpoint the type or timestamps of manipulation. Aggregating judgments can improve the detection of authenticity but does not fully address the issue of missed manipulations or the difficulty in identifying specific manipulation types, especially for audio-video combined deepfakes. AI
IMPACT Highlights limitations in current crowdsourcing methods for detecting sophisticated audiovisual manipulations.