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AI Video Detector

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Modelo:
+ 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.

    Cómo usar AI Video Detector

    1
    Introduzca su entrada

    Escriba texto, cargue un archivo o describa lo que desee. No necesita cuenta.

    2
    Haga clic en generar

    Nuestra IA procesa su solicitud en segundos utilizando los mejores modelos de código abierto.

    3
    Descargar & compartir

    Descarga, copia o comparte tu resultado. Gratis para uso personal y comercial.

    Utilice esta herramienta a través de API

    Automatice esta herramienta a partir de su propio código. Endpoint REST compatible con OpenAI, Auth Token de Portador, no requiere SDK adicional. Los costos de token coinciden con la interfaz web.

    Documentación API Obtener clave de 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}'

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    Traductor de vídeo IA
    Efectos de vídeo IA: estilo Pikaffects
    Aislador de vídeo IA

    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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