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Visual Artifact Analysis
Detects pixel-level inconsistencies, generative noise patterns, and rendering artifacts common in AI-generated video content.
Real or AI?
AI Video Detection Tool – Check If a Video Is AI-Generated or Manipulated
Estimate the risk of AI-generated, deepfake, or manipulated videos using multi-signal analysis (visual, audio, metadata). Designed for ads, UGC verification, and compliance.
Free Trial
Upgrade to Pro3 free analyses per dayResets daily
Supports:TikTokYouTubeInstagramDirect Upload
Supports TikTok, Instagram, YouTube, and uploaded videos.
Results are probabilistic and intended as a risk indicator, not absolute proof.
No signup required
Risk-based analysis
Ads & UGC compliance
Multi-Signal AI Detection
We analyze multiple layers to estimate manipulation risk
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Audio Signal Analysis
Examines audio waveforms, speech patterns, and frequency anomalies to identify synthetic voice generation or audio manipulation.
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Metadata Forensics
Inspects container formats, codec signatures, encoding timestamps, and file headers for signs of editing or tampering.
How AI Video Detection Works
Three-step process to assess video authenticity risk
1
Submit Video
Paste a URL from TikTok, YouTube, Instagram, or upload a video file directly
2
Multi-Signal Analysis
Our engine extracts frames, audio, and metadata to run detection across multiple signal types
3
Risk Assessment
Receive a probabilistic risk score with detailed breakdown of each signal analyzed
What Makes a Video AI-Generated or Manipulated
AI-generated videos are created using machine learning models such as diffusion-based generators, GANs (Generative Adversarial Networks), or transformer architectures. These tools can synthesize realistic human faces, voices, and movements that are increasingly difficult to distinguish from authentic footage. Manipulation can also occur through face-swapping (deepfakes), lip-sync alteration, or audio replacement. Each technique leaves distinct artifacts that detection systems can identify.
Why AI Videos Are Risky for Ads, UGC, and Verification
For advertisers and brands, using AI-generated content without disclosure can violate platform policies and consumer protection regulations. User-generated content (UGC) campaigns are particularly vulnerable to synthetic media submissions. In hiring, education, and verification contexts, AI-generated videos can enable fraud and impersonation. Understanding the risk level of video content helps organizations make informed decisions about usage and disclosure requirements.
When to Use AI Video Risk Assessment
- • Ad campaigns: Verify UGC submissions before publication
- • Influencer content: Assess authenticity of creator-submitted videos
- • News and journalism: Reduce risk of publishing synthetic footage
- • Remote interviews: Flag potential deepfake impersonation attempts
- • Online examinations: Detect proctoring circumvention via video manipulation
Detection Methodology
Our detection system uses a multi-model ensemble approach, analyzing visual frames through professionally-trained AI classifiers, examining audio spectral characteristics, and inspecting file metadata for encoding anomalies. Results are aggregated into a probabilistic risk score that reflects the likelihood of AI generation or manipulation—not a binary verdict.
How Results Should Be Used
- • Treat scores as risk indicators, not definitive proof
- • High-risk results warrant additional human review
- • Consider context, source, and corroborating evidence
- • Do not use results as sole basis for legal or punitive action
Privacy Notice
Uploaded videos are processed temporarily for analysis and automatically deleted. We do not store video content beyond the analysis session. URL-based analysis caches results for 24 hours to reduce redundant processing. No personal data is shared with third parties.
Assess Video Authenticity Risk
Start with a free analysis. No signup required for basic checks.