Voice cloning applies systematic style transfer rather than faithful replication, producing voices rated higher on authority and trust with reduced variance in accent and rate.
and Farid, H
5 Pith papers cite this work, alongside 348 external citations. Polarity classification is still indexing.
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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.
Face-Trace attributes synthetic face images to known generators, rejects images from unseen generators via an energy score, and clusters the rejected images into groups corresponding to distinct unknown generators, including in an incremental setting.
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.
Risk from frontier image models is driven by the convergence of photorealism, legible text, identity persistence, fast iteration, and distribution context, not photorealism alone.
citing papers explorer
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Voice "Cloning" is Style Transfer
Voice cloning applies systematic style transfer rather than faithful replication, producing voices rated higher on authority and trust with reduced variance in accent and rate.
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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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Face-Trace: Open-Set Attribution and Progressive Discovery of Synthetic Face Generators
Face-Trace attributes synthetic face images to known generators, rejects images from unseen generators via an energy score, and clusters the rejected images into groups corresponding to distinct unknown generators, including in an incremental setting.
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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
Risk from frontier image models is driven by the convergence of photorealism, legible text, identity persistence, fast iteration, and distribution context, not photorealism alone.