AI Video Detector

AI Video Detector takes a public social video URL or an uploaded file and runs it through a staged analysis pipeline that checks visual artifact signals, motion and temporal consistency, compression and encoding patterns, audio-video sync, and source detail signals. Each stage reports its own progress and confidence score independently, so partial results surface even when some signal groups cannot be reached. The final output is a likelihood-based report assembled from whichever signals were accessible — not a binary pass/fail, but a structured evidence document you review yourself.

The clearest differentiator is the evidence report format. Rather than returning a single AI-probability percentage, the tool breaks findings into six named signal groups and only shows numeric scores for signals that were actually sampled. That structure makes the report defensible — a journalist or moderator can cite which artifact categories were checked and what confidence level each returned, rather than pointing to a black-box score.

The tool fits a manual review workflow: a fact-checker spots a suspicious clip, runs it through before deciding whether to publish or escalate, and keeps the report as documentation. It breaks when the volume of videos requiring review exceeds what a person can submit one at a time — there is no API, no batch upload, and no integration path. Teams moderating content at scale will exhaust the guest quota and find no programmatic way to continue; at that point the path forward is a vendor with an API or a self-hosted detection pipeline. Link scanning for Instagram and YouTube is described in the docs as returning limited results, so any workflow depending on those platforms needs upload access to the original file.