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AI Video Detector

Ho sebelisoa ka khoebo 380+ li-models Ha ho letšoao la metsi Ha ho hlokahale ho ngolisoa
Mofuta:
+ GPT-5, Claude, Gemini
Upload a clip and we sample a handful of frames, checking each for the tells of AI generation and face-swapping — warped features, melting edges, flickering detail, impossible physics. You get an overall verdict plus a per-frame breakdown. Honest note: modern video models (Sora, Veo, Kling, Runway) are hard to catch and skilled deepfakes harder still. Treat this as one signal, never as proof.

Drop a video to analyze

MP4, WebM, MOV up to 50MB — shorter clips score faster

~3,000 tokens
Sampling frames… 0 / 0

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Signals across frames:
    Per-frame scores:

    This is an AI-powered estimate from sampled frames, not a court-admissible forensic test. False positives and false negatives happen — use judgment alongside it.

    When to check a video

    Newsroom verification

    Vet a viral clip before you republish it. Pair frame-sampling with reverse-image-search on keyframes and InVID metadata checks. One signal among several, never proof on its own.

    Deepfake / face-swap

    Check whether a face in a video was swapped — look for boundary flicker around the jaw, mismatched skin tone, teeth that warp between frames. Switch the mode chip to Deepfake for a face-focused pass.

    Catfish / romance scams

    A short selfie video is harder to fake than a photo, but AI avatars exist. Sample it before trusting a profile. A live, unscripted video call is still the gold standard.

    Education / submissions

    Screen submitted video assignments for AI generation before a human review. Tells you a clip is likely synthetic; cannot prove which tool made it. Never the sole basis for an integrity charge.

    Honest about limits

    Top-tier models (Sora 2, Veo 3, Kling 2, Runway Gen-4) produce clips that pass frame-level checks. Re-encoded, cropped, or short clips are harder to score. C2PA content credentials, where present, are more reliable than visual heuristics.

    How it works

    Everything runs in your browser up to the frame check: we grab evenly-spaced frames from your clip, send only those still images to our vision model, and average the results. The video itself never leaves your device as a file — only the sampled frames are analyzed.

    Text AI Detector Image AI Detector Fumana Tokens
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    Ngola mahala

    Ho sebetsana le kopo ea hau...

    Upload a clip and find out whether it was AI-generated or deepfaked. We sample frames and check each for diffusion artifacts and face-swap tells. Free, no account.

    Mokhoa oa ho sebelisa AI Video Detector

    1
    Ke eng eo u e kentseng?

    Tlatsa mongolo, kenya faele, kapa hlalosa seo u se batlang. Ha ho hlokahale ak'haonte.

    2
    Tobetsa ho theha

    AI ea rona e sebetsana le kopo ea hau ka metsotsoana ka ho sebelisa li-models tse ntlehali tsa open-source.

    3
    Tlosa & & arolelana

    Kopitsa, kenya kapa arolelana litlamorao tsa hau. Haholo-holo bakeng sa ho sebelisana le batho ba bang le ho rekisa.

    Senya sesebelisoa ka API

    E-ba le sesebelisoa sena ka ho iketsetsa ho tloha ho kotloloho. OpenAI-compatible REST endpoint, Bearer-token auth, ha ho hlokahale SDK e eketsehileng. Litheko tsa token li lumellana le interface ea webosaeteng.

    Litokomane tsa API Fumana konopo ea API 
    curl -X POST https://api.free.ai/v1/video/generate/ \
  -H "Authorization: Bearer sk-free-..." \
  -H "Content-Type: application/json" \
  -d '{"prompt": "A cat playing piano", "duration": 4}'

    Li-Tools tsa AI tse Bulehileng tse Bulehileng

    Moetsi oa livideo tsa AI
    Motsamaisi oa Livideo
    Motšehali oa Video oa AI
    Studio ea ho arolelana livideo tsa AI
    Ho nka liphetho tsa AI (Runway Act-Two)
    Motsamaisi oa Litšoantšo
    Liphetho tsa Video ea AI - Pikaffects-style
    Motsamaisi oa Video ea AI

    AI Video Detector — FAQ

    Upload a clip and we sample a handful of evenly-spaced frames, run each through our vision model, and combine the results into one verdict — a probability score, a confidence badge, the signals spotted across frames, and a per-frame score strip. Two modes: AI-generated video detection and deepfake face-swap detection.

    No. Frame sampling happens entirely in your browser: we grab still frames from the clip and send only those images to the detector. The video file itself never leaves your device. That keeps it fast and private — only the sampled frames are analyzed, then discarded.

    Honest answer: modest. It does well on older or lower-effort AI video and obvious face-swaps, but the current top models (Sora 2, Veo 3, Kling 2, Runway Gen-4) produce clips that frequently pass a frame-level check. Treat the score as one signal, never as proof. False positives and false negatives both happen.

    Because it analyzes individual frames as images, not motion over time. A single AI frame can look photoreal even when the temporal behavior (flicker, morphing, unstable detail between frames) gives it away to a human. We surface a per-frame breakdown so you can eyeball consistency yourself, but dedicated temporal forensics goes deeper than what runs in a browser.

    Per frame: warped or melting edges, smeared fine detail, garbled text and logos, impossible reflections and shadows, plastic-looking skin, extra or fused fingers, and diffusion-style texture artifacts. In deepfake mode it weights face-boundary flicker, mismatched skin tone at the jaw/hairline, and teeth or eyes that warp between frames.

    AI-generated video checks whether the whole clip was synthesized by a text-to-video model. Deepfake face swap checks whether a real video had a different face composited onto it. The tells differ — switch to Deepfake mode when you suspect a swapped face on otherwise-real footage.

    No. Courts require provenance-based verification — C2PA content credentials, cryptographic signing, original-source chain-of-custody. Visual heuristic detection has too high a false-positive rate to be admissible. Use this for a casual sanity check and pair it with proper forensics for anything high-stakes.

    Fewer frames means fewer samples to average, so the verdict is shakier — the confidence badge drops to low. Heavy re-encoding (social-media re-compression, screen recording) also smooths over the very artifacts the detector relies on, which can hide a synthetic clip or, less often, make a real one look suspicious. Upload the highest-quality original you have.

    The same vision-language model behind our image detector (Qwen 2.5-VL 7B), prompted for forensic-style artifact analysis. Each sampled frame is scored with a structured JSON schema (probability + verdict + confidence + signals), then aggregated across frames.

    Some generators and cameras embed C2PA signatures that declare how a clip was made — that is the more reliable path than visual heuristics. We do not yet read C2PA from video. Where a clip carries credentials, verify them at verify.contentauthenticity.org alongside our check.

    Hive and Sensity are B2B deepfake-detection APIs priced for enterprise; Deepware offers a free scanner focused on face-swaps. Free.ai gives you a free, no-signup browser check across both AI-generation and deepfake modes with a per-frame breakdown. For legal or brand-safety determinations, the dedicated B2B forensics vendors go deeper — we are a transparent first-pass tool.

    The page samples frames client-side and calls /v1/image/describe/ per frame (mode=ai-detect or deepfake-detect). To automate, extract frames yourself (ffmpeg) and POST each to that endpoint, then average ai_probability across frames. Bearer auth, 10K tokens/month free. /api/ has the curl example.

    Ngolisa mahala bakeng sa 30,000 tokens

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