| What it does | Identify people by how they move using biomechanical AI, even when faces aren't visible
| Browser fingerprint and automation detector that cross-checks JavaScript consistency against network layer signals
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| Stage | Not given
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| Founded | Not given
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| Based in | IL
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| Pricing model | Not given
| Free
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| Key features | - Identify known people from video galleries or watchlists
- Determine if two videos show the same person
- Re-identify people within and across videos
- Works with low-resolution video and long distances
- Functions despite occlusions, masks and appearance changes
- Analyzes ground camera video feeds
- Does not rely on facial recognition
- Standalone deployment, integration, or OEM embedding options
| - JavaScript fingerprinting across canvas, WebGL, WebGPU, audio, fonts, navigator and screen
- Network layer analysis via Cloudflare request metadata
- Behavioral challenge to verify human interaction
- Open-source package for CI integration
- Passive automation detection via navigator properties and framework residue
- Cross-layer consistency checking between JavaScript and network signals
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| What makes it different | - Based on 20+ years of biomechanics research from Ben-Gurion University
- Requires only a few usable walking steps to create a biomechanical signature
- Works without facial recognition where other systems struggle
- Founded by researchers with 20+ years biomechanics expertise and successful startup founders
| - Three independent detection axes that fail separately rather than as one system
- Combines JavaScript analysis with network-layer observations that JavaScript cannot fake
- Educational and defensive tool, not a service for evading detection
- Live read-only measurement with no data storage
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| Community votes | 0 | 0 |
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| DR (Domain Rating by Ahrefs) | 10 ▲ +3 (up) since last check | 6 ● no change since last check |
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| Trust Flow (Majestic) | 6 | 2 |
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| Citation Flow (Majestic) | 7 | 27 |
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| Referring domains (Majestic) | 21 | 42 |
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MotionAnalytics
liarjs.dev