Forensic Output Scale (FOS): Dataset of 79 Deepfake Detection Methods Classified by Forensic Output Type

This dataset accompanies the paper "Detecting Deepfakes for the Courtroom: A Multi-Modal Forensic Output Framework" (EAI CSECS 2026). It catalogs 79 deepfake detection methods across image, video, audio-visual, and audio modalities, classified by the Forensic Output Scale (FOS), a five-level taxonomy of detector output types: L0 Detection (score/probability), L1 Localization (where), L2 Attribution (what generator/method), L3 Narration (natural-language explanation), and L4 Verification (a specific, juror-checkable factual claim). The dataset reveals that 57% of surveyed methods (45/79) ship only L0 outputs, while no method currently produces L4 verification output. Seven methods compute L4-capable internal signals (mouth temporal anomalies, action unit relationships, blood-flow rPPG, lip-audio alignment, pupil geometry, etc.) but collapse them to scalar scores at inference — a gap we term the "L4 readiness gap." Contents (10 sheets): README — taxonomy definitions and dataset documentation Methods — master catalog of all 79 methods with year, venue, modality, FOS level, output type, internal signal (if L4-capable), notes, and Chicago author-date reference L0 Detection / L1 Localization / L2 Attribution / L3 Narration / L4 Verification — methods split by FOS level L4-Capable (ships L0) — the seven detectors with internal verifiable signals plus the L4 claim each would produce if surfaced and how a juror could verify it Datasets — 21 evaluation datasets supporting explainable deepfake detection Summary — distribution statistics across FOS levels and modalities Intended use: Forensic practitioners selecting court-admissible detectors, researchers identifying gaps in explainability, and policy/legal scholars assessing detector suitability under Daubert and analogous evidence standards.