Detection Methodology
How GhostScreen detects AI-generated video in real-time. Our 5-method ensemble approach, benchmark results, and technical specifications.
You don't need to understand how we detect deepfakes to use GhostScreen. But if you're the kind of person who wants to know what's under the hood, this is for you.
We built our detection system on peer-reviewed research and real-world testing. Here's exactly how it works.
How It Works
GhostScreen uses a weighted ensemble of 5 independent detection methods, each analyzing different aspects of video authenticity. All processing happens locally in your browser using established computer vision techniques.
Scoring System
Composite score calculated from weighted average of all 5 detection methods. Confidence increases with more signals detected.
5 Detection Methods
Each method targets specific artifacts common in AI-generated video. Combined, they provide robust detection across multiple deepfake generation techniques.
1. Boundary Artifacts
25% WeightDetects unnatural blending at face edges where AI-generated faces merge with backgrounds or original video.
Signals Detected
- - Face edge blending artifacts
- - Gaussian blur at boundaries
- - Color discontinuities at jawline/hairline
- - Alpha channel errors
Why It Works
DeepFaceLive, FaceSwap, and similar tools often produce visible seams where the synthetic face meets the original frame.
2. Eye Texture Analysis
20% WeightAnalyzes iris texture complexity and eye characteristics that AI often fails to reproduce accurately.
Signals Detected
- - Iris texture entropy (threshold: 3.5)
- - Specular reflection presence
- - Pupil darkness levels
- - Sclera brightness consistency
Why It Works
Real human irises have complex, high-entropy texture patterns (>3.5). AI-generated eyes often appear smoother with less detail.
3. Temporal Consistency
20% WeightTracks movement patterns across frames to detect the unnatural smoothness or glitches typical of AI interpolation.
Signals Detected
- - Frame-to-frame movement variance
- - Landmark tracking glitches (>15px jumps)
- - Repetitive pattern detection
- - Optical flow anomalies
Why It Works
Natural human movement has variance between 0.02-0.15 pixels per frame. AI interpolation is often too smooth or contains sudden jumps.
4. Blink Pattern Analysis
15% WeightMonitors natural blinking behavior using Eye Aspect Ratio (EAR) calculations to detect synthetic patterns.
Signals Detected
- - Blink rate (natural: 10-30/minute)
- - Blink duration (natural: 100-400ms)
- - Timing variance patterns
- - EAR threshold: <0.2 = closed
Why It Works
Many deepfakes trained on still images fail to reproduce natural blinking. AI avatars often blink too regularly or too rarely.
5. Lighting Physics
20% WeightValidates that face lighting is physically consistent with the environment and follows natural illumination patterns.
Signals Detected
- - Face-to-background lighting ratio (0.5-2.0 natural)
- - Vertical gradient (top-to-bottom: 0.8-1.5)
- - Shadow asymmetry
- - Color grading mismatches
Why It Works
When a synthetic face is composited onto real video, the lighting often doesn't match the environment.
Benchmark Methodology
How we test and validate detection accuracy across multiple deepfake generation methods.
Test Dataset Composition
Test Conditions
Accuracy Results
Current performance metrics from internal testing. We're transparent about what our technology can and cannot do.
Honest Limitations
- - Detection accuracy decreases with low-quality video (<480p) or extreme lighting conditions
- - Sophisticated, well-lit AI avatars may occasionally pass detection
- - Natural face filters and virtual backgrounds can trigger false positives
- - Performance depends on consistent face visibility during the call
- - We recommend using detection results as one signal among many in your hiring decision
Technical Specifications
Processing
| Analysis Interval | 6 seconds |
| Frame Processing | <2 seconds |
| Input Resolution | 224x224px |
| Frame History | 25 frames |
Models
| Face Detection | TinyFaceDetector |
| Landmarks | 68-point model |
| Model Size (Face) | ~189KB |
| Model Size (Landmarks) | ~349KB |
Privacy & Compliance
| Data Storage | None |
| Cloud Upload | None |
| GDPR Compliant | Yes |
| CCPA Compliant | Yes |
Ready to see it in action?
Schedule a demo to see GhostScreen detect AI-generated video in real-time.