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AI Video Detector

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Model:
+ 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.

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    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.

    Kako koristiti AI Video Detector

    1
    Unesite svoj unos

    Unesite tekst, učitajte datoteku, ili opišite šta želite. Nema potrebe za računom.

    2
    Kliknite da generirate

    Naša UI obrađuje vaš zahtjev u sekundama koristeći najbolje modele otvorenog koda.

    3
    Preuzmi i podijeli

    Preuzmite, kopirajte ili podijelite svoj rezultat. Besplatno za ličnu i komercijalnu upotrebu.

    Koristi ovaj alat preko API-ja

    Automatizirajte ovaj alat iz vlastitog koda. OpenAI-kompatibilna REST krajnja tačka, Bearer-token autentifikacija, nije potreban dodatni SDK. Token troškovi odgovaraju web sučelju.

    API dokumentacija Pribavi API ključ 
    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}'

    Srodni besplatni UI alati

    UI Video Generator
    Video editor
    UI Video Enhancer
    UI Video Dub Studio
    UI Performance Capture (Runway Act-Two)
    UI Video prevodilac
    UI video efekti - Pikaffects-style
    UI Video Upscaler

    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.

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