Video moderation
Video moderation is the process of screening video for material that breaks a platform's rules or the law, such as nudity, sexual content, violence, hate symbols or abusive language, and then acting on it. Automated systems sample frames, transcribe the audio and run classifiers that score each signal by category and timestamp. The platform compares those scores with its own policy to publish, restrict, blur, remove or send a video to a human reviewer. Most systems combine AI screening for volume with people for borderline calls and appeals.
How does AI video moderation work?
A video is too long to classify as one unit, so moderation breaks it into signals. Frames are sampled at a fixed interval or at scene changes, and each sampled frame goes through image classifiers trained on categories such as nudity or graphic violence. The audio is transcribed, and the transcript is checked for profanity, slurs or threats. Some systems also read on-screen text with OCR, since a caption or sign can break the rules even when nobody speaks.
Each signal returns a score or a flag with a timestamp, so a reviewer can jump to the moment that triggered it. Those results are rolled up into a decision for the whole video.
The decision itself belongs to the platform. A family community app and a news desk handling conflict footage can receive the same flags and act on them in opposite ways. The policy, thresholds and review queue are product choices, not model settings.
Why is video moderation harder than image moderation?
An image is one frame. A ten-minute video at thirty frames a second is about eighteen thousand frames plus a soundtrack. Running a classifier on every frame costs too much, so systems sample, and sampling creates a gap: a few frames of prohibited content between samples can slip past. Denser sampling around scene changes narrows that gap at extra compute cost.
Context is spread over time. A single frame of a medical procedure, a news report or a film scene may look the same as harmful content; the surrounding seconds and the audio explain which it is. Classifiers that see one frame at a time cannot always tell.
Video also arrives through more paths than images: uploads, live streams, clips cut from other videos and thumbnails picked from frames. Each needs its own place in the pipeline. Live streams are the hardest, because there is no finished file to check before viewers see it.
Pre-publication vs. post-publication video moderation
Pre-publication moderation holds a video until screening finishes. Nothing reaches viewers unchecked, but every upload waits. The wait is shorter when moderation runs in parallel with encoding instead of after it, since the video cannot play until encoding is done anyway.
Post-publication moderation lets the video go live straight away and removes or restricts it if it is flagged. Creators see their posts appear instantly, but viewers can see harmful content for the minutes it takes to screen and act.
Many platforms mix the two by trust level. New or unverified accounts are held for screening; established creators publish immediately and are checked after. Apps for children and family communities usually hold everything that is flagged until a person has looked. Async interview and proctoring platforms tend to flag for review and never reject automatically, because a wrongly marked candidate is a serious error.
How accurate is video moderation?
Moderation accuracy is a trade between two kinds of mistake. A false negative lets harmful content through. A false positive removes or hides something that was fine, such as a swimwear tutorial, a breastfeeding video or a news clip. Tightening the threshold catches more harm and wrongly flags more legitimate posts; loosening it does the reverse.
No single setting suits every platform, which is why the threshold should be the platform's choice. The right test is your own content: run a sample of past uploads through the system and compare its flags with what your reviewers actually decided.
Human review handles the cases models get wrong: satire, art, education, medical content, reclaimed slang, and new kinds of abuse a model has not seen. A review queue sorted by flag type and timestamp lets moderators jump straight to the flagged moment instead of watching every video from the start.
How does FastPix handle video moderation?
FastPix handles video moderation inside in-video AI, which runs in parallel with encoding on every upload. Moderation returns NSFW and profanity flags based on sampled frames and audio. It does not block publication by itself, and there is no user-set threshold inside FastPix. The flags are signals, and your platform applies its own policy.
A typical pipeline looks like this. A user uploads through the resumable web, iOS, Android or Flutter SDKs. Encoding and in-video AI start on the same upload. When processing finishes, a webhook, signed with HMAC SHA-256, tells your backend the video is ready. Your code reads the moderation flags and decides: publish, hold, restrict or send to your review queue. Until that decision, the video can sit behind a signed playback ID, so only reviewers with a valid token can play it. The same in-video AI output carries the transcript with word-level timestamps, which your reviewers can use to find the spoken words behind a profanity flag.
The documented flag categories are NSFW and profanity. Screening for other categories, such as graphic violence, needs a separate classifier in your pipeline.
Moderation is billed per minute of video processed, on the same bill as encoding, with no token billing and no separate AI invoice. Standard encoding is free up to 4K. The free plan's AI sample minutes, no card needed, cover a first test on real uploads.
