Participants achieved near-chance accuracy (~50%) distinguishing real from AI-generated media across four modalities, with performance declining for faces, foreign languages, single modalities, and mixed-authenticity audiovisual clips.
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EMSFD uses Dirichlet-based evidence modeling to capture prediction uncertainty in synthetic face detection and applies uncertainty-driven active learning to achieve 15% higher accuracy than prior methods.
Frontier image models enable synthetic visual evidence that erodes trust in photos through combined realism, text, and identity features, calling for layered technical and policy controls.
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As Good As A Coin Toss: Human detection of AI-generated images, videos, audio, and audiovisual stimuli
Participants achieved near-chance accuracy (~50%) distinguishing real from AI-generated media across four modalities, with performance declining for faces, foreign languages, single modalities, and mixed-authenticity audiovisual clips.
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Evidence-based Decision Modeling for Synthetic Face Detection with Uncertainty-driven Active Learning
EMSFD uses Dirichlet-based evidence modeling to capture prediction uncertainty in synthetic face detection and applies uncertainty-driven active learning to achieve 15% higher accuracy than prior methods.
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Seeing Is No Longer Believing: Frontier Image Generation Models, Synthetic Visual Evidence, and Real-World Risk
Frontier image models enable synthetic visual evidence that erodes trust in photos through combined realism, text, and identity features, calling for layered technical and policy controls.
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