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Toward Calibrated, Fair, and accurate Deepfake Detection

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arXiv:2606. 09881v1 Announce Type: new Abstract: Deepfake detectors show large performance gaps across demographic groups.

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Authors: Ryan Brown, Chris Russell
Released: 2026-06-10 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Toward Calibrated, Fair, and accurate Deepfake Detection overlaps with clean result "The marker is a representational handle, not a behavioural one — sharing it between a villain persona and the assistant transfers no misalignment (HIGH confidence)", experiment "Follow-up to #354: cascading chunk-binding — does A→B, B→C, C→D propagate the full chain on a recipient trained only to emit A?", experiment "#351 follow-up: broader-vocab position-0 sweep at T=1.0 + position-1 suffix isolation". Matching terms: rate, does, test. Source: arxiv cs.LG (Machine Learning).
arXiv: 2606.09881

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