Field guide
How AI video detection works—and where it stops.
Useful detection is a chain of independent observations: file structure, representative frames, motion over time, audio, and provenance. None of them alone establish the truth of a video.
1. Start with a bounded, decodable clip
A safe service inspects its container before decoding: duration, streams, codec, dimensions, and whether the file actually has video. Limits make the analysis predictable and prevent a disguised or oversized media file from exhausting the worker.
2. Sample across time, not just at the beginning
Generative artifacts can be uneven. Representative frames distributed through a short clip are scored individually, then displayed as a timeline so a reviewer can see whether a signal persists or appears around one edit.
3. Keep temporal evidence separate
Frame-to-frame and second-order motion measures can point to unusual transitions, but they can also react to camera shake, compression, cuts, or lighting changes. They are evidence for review, never a stand-alone fake verdict.
4. Include audio and provenance coverage
An audio track, editor tag, or Content Credential can provide important context. Each is independent: a missing watermark does not prove a recording is natural, and missing C2PA is common after platform processing.
5. Report partial coverage honestly
A meaningful video report lists what ran, what failed, and what cannot be concluded. If a clip has no strong warning signal, the appropriate result is inconclusive—not verified human origin.
Try a bounded, explainable video screen.
Upload one short MP4, WebM, or AVI clip. AI Screener makes the available evidence and limitations visible rather than promising a universal detector.
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