AI Video Detector: Check If a Video Is AI-Generated
Upload a video to check for AI generation, deepfakes and manipulation. AIDetector.cx evaluates visual, temporal, audio, metadata and provenance signals without treating one anomaly as absolute proof.
AI Video Detector & Deepfake Forensic Suite
Detect and localize synthetic video manipulation, voice cloning, viseme lip-sync misalignment, generator fingerprints, and C2PA Content Credentials.
Requires strong, multi-modal convergence before declaring synthetic manipulation. Optimized to prevent false positives on creators, low-light footage, and heavy platform compression.
Calibrated to catch early-generation artifacts, subtle face morphing, and gentle inpainting. Carries a higher false-positive risk under social media compression.
Comprehensive cross-modal analysis including frame-by-frame Fourier spectral residuals, optical flow motion fields, C2PA cryptographic chain of custody, and alternative explanation modeling.
Upload Video for Multi-Modal Forensic Analysis
Supports MP4, WebM, MOV, AVI, and MKV up to 250MB. Preserves original bytes for C2PA provenance validation.
Silent UI screen recording without human faces or audio stream. Demonstrates N/A gating and zero AI false-positive penalty.
Screen recording with genuine microphone speech voiceover. Tests authentic audio stream and speech forensic verification.
Screen recording of desktop browser playing an AI-generated video. Separates capture method (Screen) from content (AI Video).
Authentic camera footage with natural sensor noise, acoustic reverberation, and C2PA camera metadata.
What Is an AI Video Detector?
An AI video detector is a specialized multi-modal software pipeline that analyzes video frames, audio tracks, temporal motion vectors, and binary container metadata to determine whether a video was synthesized or manipulated with artificial intelligence models.
How to Check If a Video Is AI-Generated
Follow this rigorous five-step verification procedure to evaluate video authenticity:
Upload Original File
Always analyze original uncompressed footage when available. Recompression degrades high-frequency residuals.
Select Mode
Use Balanced Mode to avoid false positives, or Forensic Mode for detailed timestamped timelines.
Inspect Pipeline
The engine evaluates optical flow, phoneme-viseme speech sync, spectral voice cloning, and C2PA manifests.
Review Timeline
Examine flagged intervals (e.g., 00:14–00:18 lip-sync mismatch) to localize alterations.
Verify & Export
Check camera provenance, review alternative legitimate explanations, and export a certified PDF evidence report.
How Do AI Video Detectors Work?
The AIDetector.cx forensic engine executes a 17-stage asynchronous multi-modal pipeline combining spatial frame analysis, temporal physics, audio acoustics, and cryptographic verification:
Computes one-way SHA-256 fingerprints to guarantee audit integrity and prevent unauthorized byte tampering.
Segments continuous scenes, isolates camera cuts, and samples keyframes adaptively based on motion complexity.
Calculates pixel velocity vectors across sequential frames to detect generative morphing, physics glitches, and texture swimming.
Tracks corneal reflection glints, pupil geometry, ear lobe persistence, and micro-expressions across head rotations.
Compares audio speech phonemes with visual mouth movements (visemes) to flag automated AI lip-sync modifications.
Parses binary JUMBF boxes to validate digital signatures from camera hardware and creative editing suites against root CAs.
What the Detector Analyzes
Our multi-signal framework categorizes forensic observations into five core evidentiary pillars:
Visual & Spatial Evidence
- Skin & Hair Textures: Detects over-smoothed diffusion skin and unnatural hair strand blending.
- Lighting & Shadows: Identifies conflicting light sources, missing shadow cast angles, and invalid specular reflections.
- Anatomical Coherence: Evaluates finger geometry, ear shape symmetry, and teeth alignment.
- Scene Perspective: Flags warping background lines, floating objects, and distorted text signs.
Temporal & Motion Evidence
- Frame-to-Frame Persistence: Detects flickering textures, disappearing background objects, and sudden morphing.
- Identity Stability: Monitors facial feature consistency across extreme head turns and partial occlusions.
- Motion Continuity: Evaluates realistic gravity and momentum dynamics against synthetic generation warps.
- Frame Interpolation: Spots generative frame blending artifacts and duplicate frame insertion patterns.
Audio & Acoustic Evidence
- Voice-Cloning Signatures: Identifies robotic spectral harmonics, absent breath pauses, and synthetic vocoder artifacts.
- Cadence & Inflection: Evaluates natural speech rhythm against monotone text-to-speech outputs.
- Room Acoustics: Detects audio tracks recorded in anechoic environments pasted over reverberant video spaces.
- Acoustic Splicing: Highlights abrupt background noise cuts and audio track boundary manipulations.
Audio-Visual & Metadata Provenance
- Phoneme-Mouth Sync: Inspects whether audio plosives (P, B, M) align with visual lip closures.
- C2PA Content Credentials: Cryptographically validates author claims and editing history against root CAs.
- Container Headers: Analyzes codec parameters, encoder history, and creation timestamps.
- Missing Metadata Warning: Notes that stripped metadata (common on social platforms) is not proof of AI creation.
AI-Generated Video vs. Deepfake Video
Understanding the difference between fully generated synthetic scenes and targeted deepfake manipulation is essential for proper risk assessment:
| Category | Technology & Workflow | Key Forensic Indicators | Primary Detector Finding |
|---|---|---|---|
| Fully AI-Generated Video | Text-to-Video / Image-to-Video (Sora, Kling, Runway, Veo, Luma) | Temporal physics glitches, texture swimming, generative noise across whole scene | Fully AI-Generated |
| Deepfake Face Swap | Replacing real subject face with target persona (Roop, SimSwap, DeepFaceLab) | Facial boundary blending artifacts, mismatched skin tone at jawline, corneal glint divergence | Face Swap Detected |
| AI Lip-Sync Modification | Dubbing real speaker with modified audio and generative mouth re-animation (Wav2Lip) | Phoneme-viseme temporal mismatch, mouth area blurring, static upper face | AI Lip-Sync Manipulation |
| Voice Clone Over Real Video | Authentic video footage paired with cloned synthetic voice audio | Audio spectral synthetic harmonics, room acoustic mismatch, absent breath acoustics | Synthetic Voice Over Authentic Video |
| Conventional Video Editing | Cuts, color grading, transitions, audio equalization (Premiere, Final Cut) | Coherent optical flow, natural camera sensor noise, valid audio acoustics | Authenticity Supported |
Detecting Sora, Veo, Kling, Runway and Other Generators
We continuously benchmark AIDetector.cx against premier commercial and open-weights video generation architectures. Our transparent test matrix reflects real empirical performance and known technical boundaries:
| Generator | Tested Version | Detection Status | Known Limitations | Last Tested |
|---|---|---|---|---|
| OpenAI Sora | Sora v1.0 & Sora 2 Pre-release | Verified Robust | High-bitrate static landscape shots require temporal optical flow inspection | Aug 2026 |
| Google Veo | Veo 1080p Public Release | Verified Robust | Requires >2 seconds duration for accurate motion trajectory modeling | Aug 2026 |
| Kling AI | Kling 1.0, 1.5, & 2.0 | Verified Robust | Motion interpolation smoothing can reduce residual high-frequency traces | Sep 2026 |
| Runway | Gen-2 & Gen-3 Alpha | Verified Robust | Video-to-video style transfers with subtle weight may yield inconclusive scores | Aug 2026 |
| Luma Dream Machine | Dream Machine 1.5 | Verified Robust | Fast dynamic camera pans require adaptive keyframe sampling | Jul 2026 |
| Hailuo / MiniMax | Video-01 HD | Verified Robust | Complex crowd backgrounds flagged with slightly reduced confidence bounds | Aug 2026 |
| Hunyuan & Wan | HunyuanVideo & Wan2.1 | Verified Robust | Open-weights fine-tunes with custom LoRAs require multi-modal consensus | Aug 2026 |
| HeyGen & Synthesia | Avatar 4.0 / Expressive 2.0 | Verified Robust | Excellent detection via phoneme-viseme alignment and corneal reflection glints | Sep 2026 |
| Adobe Firefly Video | Beta Model | Verified Robust | Native C2PA Content Credentials parsed automatically for instant confirmation | Jul 2026 |
Important note on attribution: Detecting that a video is synthetic (identifying mathematical diffusion artifacts) is fundamentally different from attributing it to a specific generator brand. When explicit metadata or watermarks are absent, our engine flags synthetic origin while transparently marking the specific generator as Unknown Synthetic Process.
AI Video Detection After Compression
When videos are shared across TikTok, WhatsApp, YouTube, and Instagram, aggressive transcoders discard high-frequency pixel data. AIDetector.cx is calibrated to navigate compression without making rash false accusations:
TikTok & Instagram: Heavy macro-block quantization and variable framerate (VFR) conversions destroy natural camera sensor noise while creating blocky edges that mimic synthetic seams.
WhatsApp & Telegram: Extreme downscaling (often to 480p/720p) strips subtle facial texture details, requiring reliance on temporal optical flow rather than single-frame texture filters.
Quantization Compensation: Our engine measures the discrete cosine transform (DCT) blockiness level. If degradation is severe, the detector automatically widens uncertainty intervals.
Honest Inconclusive Returns: If a video has been screen-recorded multiple times or compressed below forensic usability thresholds, the system returns Insufficient Quality / Inconclusive rather than guessing.
Original-versus-Published Video Comparison
Content creators often have genuine, authentic camera footage falsely flagged as AI when re-uploaded by third parties. Our dual-video comparator proves authenticity by isolating compression artifacts from the source master:
Aligns timestamps to demonstrate that published video anomalies stem strictly from platform downscaling rather than generative insertion.
Verifies original waveform integrity against recompressed or background-music-replaced social media uploads.
Extracts original camera EXIF, lens hardware profiles, and editing export histories present only in the author's master file.
Generates a certified side-by-side forensic PDF report to appeal erroneous platform strikes on YouTube, TikTok, or Instagram.
Are AI Video Detectors Accurate?
No AI video detector is 100% infallible. Accuracy is heavily influenced by video duration, lighting, face visibility, generator family, and recompression. Here is our verified benchmark evaluation conducted across 850 multi-modal video samples (August 2026):
Benchmark Methodology: Evaluated on 850 video clips (425 authentic footage from Sony FX3, iPhone 15 Pro, RED Komodo; 425 synthetic clips generated via Sora, Kling 1.5, Veo, Runway Gen-3, Wav2Lip, and SimSwap). Video lengths ranged from 4s to 60s at resolutions between 720p and 4K.
Can AI Video Detectors Be Bypassed?
Adversarial techniques such as extreme re-encoding, injecting synthetic Gaussian noise, heavy film grain overlays, and screen-recording can degrade single-frame classifiers. AIDetector.cx is architected defensively to resist evasion:
Controlled Examples & Benchmark Ground Truth
Explore 12 controlled video test cases with known transformation histories, expected classifications, detected evidence signals, and technical limitations:
Direct camera sensor export; no transcoding or post-filters applied.
Natural optical sensor PRNU noise, coherent optical flow vectors, valid EXIF lens metadata, intact room acoustics.
What Makes a Reliable AI Video Detector?
When evaluating AI video detection solutions for enterprise or journalistic workflows, look for these foundational technical criteria:
Must not falsely accuse authentic human creators due to standard editing, cosmetic makeup, or low-light video sensor noise.
Must provide exact timestamp intervals (e.g. 00:14–00:18) rather than an unhelpful single boolean label for the entire clip.
Must cross-correlate speech audio acoustics with visual lip movements to catch audio dubbing and voice cloning.
Must read and cryptographically validate Content Credentials from certified cameras and creative applications.
Must state when evidence is insufficient or inconclusive rather than forcing a random binary guess.
Must evaluate in volatile memory and purge video frames immediately to protect confidential and unreleased footage.
Video-Call and Livestream Deepfake Detection
Real-time deepfake defense requires strict technical transparency. Web browsers cannot silently intercept external desktop applications (like Zoom or Teams) without explicit user permissions:
Live-Call Protection is currently being validated. Our browser module analyzes video streams strictly from permitted device cameras, microphones, or user-shared screens and tabs via WebRTC.
The system inspects real-time optical flow, challenge-response liveness cues (e.g., prompt head turns, face occlusions), and virtual camera driver injection flags without displaying simulated telemetry.
AI Video Detection for KYC and Fraud Screening
Designed to support KYC, identity verification, and fraud-screening workflows against sophisticated synthetic presentation attacks:
Flags generated video avatars and face reenactments submitted to automated selfie video verification workflows.
Detects software virtual webcams (OBS virtual cam, ManyCam) used to bypass hardware camera security checks.
Identifies generative inpainting and synthetic vehicle/property damage additions in submitted video proof.
Provides structured evidence reports and SHA-256 fingerprints to assist compliance officers in manual review.
Compliance notice: AIDetector.cx is designed to assist human compliance teams and must not be used as the sole automated basis for rejecting customer identities or legal verification.
AI Video Detection API
Integrate multi-modal video forensics directly into your media platforms, trust-and-safety pipelines, or KYC portals using our robust asynchronous REST API:
Our API allows high-throughput asynchronous video processing with webhook callbacks, presigned media URLs, and structured forensic JSON responses.
- Asynchronous Job Queue: Submit video files or URLs and poll or receive signed webhook payloads.
- Granular Timestamp Intervals: Get exact start and end timestamps for flagged deepfake segments.
- C2PA Manifest JSON: Programmatically access cryptographic author chains and editing assertions.
- Organization Keys & Quotas: Unified credit allocation, retention policies, and audit logs.
{
"jobId": "vjob_98412_kx92",
"status": "completed",
"assessment": {
"verdict": "deepfake_face_swap",
"calibratedScore": 89.4,
"uncertaintyLevel": "low",
"videoQuality": 88
},
"suspiciousIntervals": [
{
"start": 14.2,
"end": 18.6,
"type": "lip_sync_mismatch",
"confidence": 92.1
}
],
"provenance": {
"c2paManifestFound": false,
"sha256": "e3b0c44298fc1c149afbf4c8996fb92..."
}
}Who Should Use the AI Video Detector?
Designed to serve specialized needs across industries where video authenticity is paramount:
Verify breaking UGC footage and viral political clips before broadcasting or reporting.
Conduct forensic timeline analysis to debunk fabricated news footage and social media hoaxes.
Defend original authentic camera footage against erroneous algorithmic platform strikes.
Moderate user video uploads at scale to detect synthetic impersonation and deceptive media.
Inspect submitted video evidence for generative inpainting and fabricated property damage.
Screen automated onboarding selfie videos for deepfake face swaps and virtual camera injection.
Evaluate synthetic generative model artifacts and study media provenance standards.
Incorporate automated video authenticity checks into digital asset management systems.
Privacy and Retention
We treat uploaded video media with strict enterprise privacy protections:
Frequently Asked Questions About AI Video Detection
Find clear, technically grounded answers regarding video analysis, deepfake detection, and platform compression:
Video Authenticity & Forensic Research Hub
Deepen your understanding of synthetic media forensics with our empirical studies and technical guides:
Empirical analysis of why compression causes false positive flags on authentic creators.
How 480p downscaling affects optical flow velocity and residual noise.
Step-by-step workflow for creators to appeal algorithmic video strikes.