Multi-Model Video Authenticity Analysis for Research

Studying video manipulation at scale requires consistent, structured analysis. Manual frame-by-frame review doesn't scale, and single-model detectors produce inconsistent results across video types.

Reproducibility is the deeper methodological issue. If your study relies on deepfake detection, other researchers need to replicate your methodology. Ad-hoc tool outputs aren't reproducible — they depend on which model you used, its version, and its output format at the time of analysis. Citing 'we used a commercial detection tool' in a methods section doesn't satisfy peer review.

Ground-truth labeling also requires consistent criteria. When building a dataset of authentic versus generated video, you need inter-annotator reliability. Two researchers using different single-model tools will produce incomparable labels — making dataset merging and cross-study comparison impossible.

ConvergePanel provides structured multi-model video review with per-model evidence, consensus scoring, and exportable results — giving researchers a repeatable analysis framework rather than ad-hoc tool outputs.

The per-model evidence output uses consistent fields across every run: signals detected, confidence level, and evidence quality rating per model. You can build your dataset schema around this structure. The consensus score provides a numeric label for classification tasks; the per-model breakdown lets you study model disagreement as a research artifact in itself — useful for understanding where current AI detection methods are most uncertain.