Voxear
Advanced deepfake detection combining metadata forensics, deep learning AI models, and physics-based verification for comprehensive video authenticity analysis
Detection Examples
Simulated outputs from the three-layer pipeline — spatial landmark analysis, layer-by-layer scoring, and verdict confidence.
No manipulation artifacts detected across all three analysis layers.
#a3f2c891dFace swap artifacts detected in spatial and frequency domains.
#7b91ae43fLow AI confidence triggered physics verification layer.
#2d8f5c017Multi-Layered Detection Pipeline
Voxear implements a hierarchical detection pipeline combining three complementary approaches:
1. Metadata Forensics
First-line defense checking for AI-generation watermarks and encoding anomalies.
2. AI Detection
Xception CNN analyzing face crops with per-frame scoring, temporal anomaly detection, and Grad-CAM spatial explainability.
3. Physics Verification
Conditional checks for violations of physical laws in motion, lighting, and facial dynamics.
The system uses adaptive frame sampling and SHAP explainability to deliver transparent results suitable for forensic and research applications.
Detection Methods
Click any card to explore the technical details, algorithms, and visualizations behind each detection module.
AI Detection
Xception CNN trained on deepfake datasets, analyzing face crops with per-frame scoring, temporal anomaly detection, and Grad-CAM spatial explainability.
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Physics-Based Detection
Analyzes physical properties and motion patterns to detect violations of natural laws that deepfakes often fail to replicate correctly.
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Metadata Analysis
Examines video file metadata, encoding parameters, and digital signatures to detect AI-generated content markers and manipulation traces.
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Explainability Features
Provides transparent, interpretable insights into detection results using SHAP values, attention weights, and visual explanations.
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