False-positive handling

A signal can be wrong.
Design for correction.

False positives are expected model failures, not edge cases to hide. Review source context before any consequential decision.

Open the free checker

Scope before score.

Every result is a warning signal. The evidence and limitations below define what this route can and cannot support.

Visual media

Art, UI screenshots, stylized portraits, social compression and unseen generators can shift model scores.

Official trailers

Through the deployed lane, 3.5% of 200 official-trailer frames still drew false warnings. The set covered 70 trailers from 6 distributors unseen in training.

Video frames

A video frame is scored as a single image. One stylized, compressed or graphics-heavy frame can therefore carry a misleading signal, and motion, lip-sync and the moments between frames are never assessed.

Do not infer more than the lane measured.

  • Do not treat a raw score as a calibrated probability.
  • Provide correction and appeal paths wherever people can be affected.
  • Prefer unknown over forcing unsupported content into human or AI.

Continue with evidence

Benchmark resultsRead more →Human review policyRead more →Recruitment review workflowRead more →Try the warning-only checkerRead more →