Multi-modal Detection System

An advanced AI-powered system for detecting and tracing fraudulent content across multiple modalities (text, image, audio, video) through reverse-engineering frameworks and continuous learning of evolving generative patterns.

Features

• Multi-modal Detection: Integrated systems for text, image, audio, and video analysis
• Provenance Tracing: Track content origin and detect inconsistencies across modalities
• Reverse-Engineering Framework: Enable AI models to trace content provenance
• Advanced Cross-Modal Consistency: Detect inconsistencies using semantic similarity models
• Continuous Learning: Continuously learn evolving generative patterns
• Dynamic Evaluation System: Classify severity levels and trigger alerts for fraud signals
• Alert Engine: Configurable rules for determining when to alert on signal combinations
• Signal Combinator: Combine multiple signals using weighted strategies for enhanced detection
• Fraud Detection: Identify fraudulent content more accurately than traditional methods
• Semantic Analysis: Deep semantic understanding beyond keyword matching

Project Structure

Multi-modal Evaluation System/
├── src/
│   ├── core/                    # Core system components
│   ├── modalities/              # Modality-specific detection modules
│   ├── tracing/                 # Provenance tracing framework
│   ├── reverse_engineering/     # Reverse-engineering analysis tools
│   └── learning/                # Continuous learning systems
├── tests/                       # Unit and integration tests
├── config/                      # Configuration files
├── models/                      # Trained model files
├── datasets/                    # Training and evaluation datasets
└── docs/                        # Documentation

Installation

1. Clone the repository:

git clone https://github.com/yourusername/Multi-modal-Evaluation-System.git
cd Multi-modal-Evaluation-System

2. Install dependencies:

pip install -r requirements.txt

3. Set up environment variables:

cp config/.env.example config/.env
# Edit config/.env with your API keys and paths

Usage

Basic Detection

from src.core import MultiModalDetector

# Initialize the detector
detector = MultiModalDetector(config_path='config/detector.yaml')

# Detect fraud in content
result = detector.detect(
    text="Sample text content",
    image="path/to/image.jpg",
    audio="path/to/audio.wav",
    video="path/to/video.mp4"
)

print(f"Detection Score: {result.score}")
print(f"Is Fraudulent: {result.is_fraudulent}")

Provenance Tracing

from src.tracing import ProvenanceTracer

tracer = ProvenanceTracer()
trace = tracer.trace(content)
print(f"Provenance: {trace.origin}")
print(f"Confidence: {trace.confidence}")

Cross-Modal Analysis

from src.core import CrossModalAnalyzer

analyzer = CrossModalAnalyzer()
inconsistencies = analyzer.analyze_consistency(
    text_features, image_features, audio_features, video_features
)

Architecture

Detection Pipeline

1. Modality Extraction: Extract features from each modality
2. Individual Analysis: Run modality-specific detection
3. Cross-Modal Verification: Check consistency across modalities
4. Provenance Tracing: Trace content origin and history
5. Pattern Analysis: Apply reverse-engineering to detect generator patterns
6. Decision Fusion: Combine results for final decision

Learning System

The system continuously learns from:

• New fraudulent patterns
• Evolving generative models
• Cross-modal inconsistencies
• Provenance anomalies

Contributing

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

License

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

Acknowledgments

• Research community for multi-modal detection algorithms
• Open source frameworks and libraries used in this project