deepfake-detector-checkpoints / research_results.json
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{
"methodology_note": "RQ1 (calibration) computed on the FULL held-out validation split (11666 frames / 1049 videos), 20-pass MC-Dropout (the plugin's designed default). RQ2/RQ3 (explanation-quality correlation, triage transferability) computed on a class-balanced stratified sample (up to 2000 real + 2000 fake frames per model, drawn from the same val split) because the perturbation-based explanation-stability metric requires one forward+backward pass per sample and is not batchable -- full-scale would be many hours of single-image GPU work per model. num_perturbations=3 matches backend/main.py's actual deployed setting exactly.",
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