Veritas AI - Advanced Lie Detection System

Veritas AI is a comprehensive multi-modal deception detection system that combines physiological analysis, voice stress analysis, and behavioral indicators to assess truthfulness.

Features

• Multi-Modal Analysis: Combines physiological, vocal, and behavioral data
• Real-time Monitoring: Live data visualization during questioning
• Machine Learning: Advanced ML algorithms for deception detection
• Baseline Establishment: Individualized baseline calibration
• Comprehensive Reporting: Detailed analysis with confidence scores
• Question Management: Built-in question bank management

Installation

1. Clone the repository:

git clone https://github.com/iVGeek/veritas-ai.git
cd veritas-ai

Install required dependencies:

pip install -r requirements.txt

Usage Run the application:

python veritas_ai.py

Establish Baseline: Click "Establish Baseline" to calibrate the system for the subject

Ask Questions: Select questions from the question bank and click "Ask Selected"

Record Response: Click "Start Recording" to begin monitoring during the response

Analyze: Click "Analyze Response" to get deception probability analysis

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System Architecture

Veritas AI/
├── Data Collection Layer
│    ├── Physiological Sensors (HR, GSR)
│    ├── Audio Analysis (Voice Stress)
│    └── Behavioral Analysis (Micro-expressions)
├── Processing Layer
│   ├── Feature Extraction
│   ├── Baseline Comparison
│   └── ML Classification
└── Presentation Layer
    ├── Real-time Visualization
    └── Comprehensive Reporting

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Technical Details

Algorithms: Random Forest Classifier with feature engineering

Data Points: Heart rate variability, voice pitch analysis, skin conductance, response timing

Accuracy: Synthetic data training with cross-validation

Real-time Processing: 10Hz sampling rate with live visualization

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Ethical Considerations

⚠️ Important: This system is for educational and research purposes only. Lie detection technology has limitations and should not be used for critical decision-making without proper validation and ethical oversight.

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Contributing

Please read CONTRIBUTING.md for details on our code of conduct and the process for submitting pull requests.

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License

This project is licensed under the MIT License - see the LICENSE.md file for details.

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Citation

If you use Veritas AI in your research, please cite:

Veritas AI: A Multi-Modal Deception Detection Framework (2024)

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Support

For technical support, please open an issue on GitHub or contact the development team.

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Additional Files

.gitignore

# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class

# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
*.egg-info/

# Model files
*.pkl
*.model
CONTRIBUTING.md
markdown

Contributing to Veritas AI

We welcome contributions! Please see our development guidelines and code standards. This complete lie detection system includes:

1. Advanced GUI with real-time data visualization
2. Multi-modal analysis (physiological, vocal, behavioral)
3. Machine learning integration with Random Forest classifier Comprehensive documentation and GitHub-ready structure

Ethical considerations and proper disclaimers

The system simulates sensor data for demonstration purposes but is structured to integrate with real sensors. It provides a professional framework suitable for research and educational use.