BioCryptNet - Biometric-Conditioned Neural Encryption System

Project Overview

BioCryptNet is a novel cryptographic framework that integrates biometric identity and artificial intelligence to enhance data security and privacy. The system utilizes facial embeddings derived from deep learning-based recognition models to generate unique, user-specific encryption keys and employs a key-conditioned chaotic autoencoder to perform encryption and decryption.

Key Features

• Biometric Key Generation: Extracts unique facial embeddings from user images to serve as cryptographic keys
• Key-Conditioned Autoencoder: Neural network architecture that uses facial embeddings to encrypt/decrypt data
• Chaotic Module: Introduces controlled randomness using logistic maps to enhance security
• Multi-Modal Support: Encrypts both text and image data
• Security Analysis: Real-time entropy and statistical analysis of encrypted data

Project Architecture

Backend (FastAPI)

backend/
├── main.py                      # FastAPI application with endpoints
├── services/
│   ├── face_embedding.py       # Facial embedding extraction service
│   ├── chaotic_autoencoder.py  # Neural network models with chaotic module
│   └── encryption_service.py   # Encryption/decryption service
└── requirements.txt             # Python dependencies

Key Components:

1. FaceEmbeddingService: Uses face_recognition library to extract 128-dimensional facial embeddings
2. ChaoticAutoencoder: Implements key-conditioned autoencoders with chaotic modulation
3. EncryptionService: Handles encryption/decryption operations for text and images

Frontend (React + Vite)

frontend/
├── src/
│   ├── App.jsx                 # Main application component
│   ├── App.css                 # Styling
│   └── main.jsx                # Entry point
├── package.json                # Node.js dependencies
└── vite.config.js              # Vite configuration

Features:

• Face image upload for embedding extraction
• Text and image encryption interfaces
• Decryption with real-time results
• Security metrics visualization

Installation & Setup

Prerequisites

• Python 3.8 or higher
• Node.js 16 or higher
• pip and npm/yarn package managers

Backend Setup

1. Navigate to the backend directory:

cd backend

2. Create a virtual environment (recommended):

python -m venv venv
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

3. Install Python dependencies:

pip install -r requirements.txt

Note: The face-recognition library requires dlib, which may need additional system dependencies:

• Windows: May require Visual C++ build tools
• macOS: brew install cmake
• Linux: sudo apt-get install cmake

Frontend Setup

1. Navigate to the frontend directory:

cd frontend

2. Install Node.js dependencies:

npm install

Running the Application

Start the Backend Server

From the backend directory:

uvicorn main:app --reload --host 0.0.0.0 --port 8000

The API will be available at http://localhost:8000

API documentation (Swagger UI) is available at http://localhost:8000/docs

Start the Frontend Development Server

From the frontend directory:

npm run dev

The frontend will be available at http://localhost:3000

Usage

Step-by-Step Guide

1. Extract Facial Embedding:

   • Upload a face image through the web interface
   • The system extracts a 128-dimensional facial embedding vector
   • This embedding serves as your unique cryptographic key

2. Encrypt Data:

   • Choose between text or image encryption
   • Enter your text or upload an image
   • Click "Encrypt" - the system uses your facial embedding to encrypt the data
   • View security metrics (entropy, statistics) of the encrypted data

3. Decrypt Data:

   • Use the same facial embedding (upload the same face image)
   • Click "Decrypt" to recover the original data
   • Only the correct facial embedding can successfully decrypt

API Endpoints

• POST /api/extract-embedding: Extract facial embedding from uploaded image
• POST /api/encrypt: Encrypt text or image data using facial embedding
• POST /api/decrypt: Decrypt ciphertext using facial embedding
• POST /api/security-analysis: Analyze security metrics of encrypted data
• GET /api/health: Health check endpoint

Security Analysis

The system provides several security metrics:

• Entropy: Measures randomness in encrypted data (higher is better, >7.0 is excellent)
• Statistical Measures: Mean, standard deviation, min/max values
• Chaotic Modulation: Ensures variability even with identical keys

Technical Details

Facial Embedding Extraction

• Uses the face_recognition library (based on dlib's facial recognition model)
• Extracts 128-dimensional vectors representing facial features
• Normalized embeddings ensure consistent key generation

Key-Conditioned Autoencoder

• Image Autoencoder:

  • Encoder: Convolutional layers (64→128→256 channels)
  • Latent dimension: 256
  • Decoder: Transposed convolutions
  • Key conditioning through concatenation and addition

• Text Autoencoder:

  • Encoder: Fully connected layers (256→512→256)
  • Key conditioning: Concatenation in latent space
  • Supports variable-length text (up to 256 bytes)

Chaotic Module

• Implements logistic map: x_{n+1} = r * x_n * (1 - x_n)
• Parameter r = 3.95 (near chaos threshold)
• Modulates neuron activations by 10% using chaotic sequences
• Key-derived seeding ensures reproducibility for same user

Project Limitations & Future Work

Current Limitations

• Simplified text encryption (frequency-based encoding)
• Image decryption requires original dimensions (stored in ciphertext)
• Face recognition requires clear, frontal face images
• No training phase implemented (models use random initialization)

Future Enhancements

• Train autoencoders on large datasets for better reconstruction
• Implement more sophisticated text encoding schemes
• Add support for video encryption
• Deploy pre-trained models for better performance
• Add user management and embedding storage
• Implement secure key derivation functions
• Add support for multiple faces in one image

Security Considerations

Privacy

• No Raw Image Storage: Only embeddings (128 floats) are used, not original images
• Local Processing: All operations can be performed locally
• Embedding Protection: In production, store embeddings securely with encryption

Cryptographic Security

• This is a proof-of-concept system for research purposes
• For production use, additional security measures are required:
  • Key derivation functions (KDF)
  • Authenticated encryption
  • Secure key storage
  • Resistance analysis against known attacks

Troubleshooting

Backend Issues

• Import Errors: Ensure all dependencies are installed: pip install -r requirements.txt
• Face Recognition Errors: Install system dependencies for dlib (see Installation)
• CUDA Errors: The system works on CPU, but GPU acceleration requires CUDA setup

Frontend Issues

• CORS Errors: Ensure backend is running and CORS middleware is configured
• Connection Refused: Check that backend server is running on port 8000
• Module Not Found: Run npm install in the frontend directory

Performance

• Face Embedding Extraction: ~1-2 seconds per image (CPU)
• Text Encryption: <100ms for typical text
• Image Encryption: ~500ms-1s for 128x128 images (CPU)
• Decryption: Similar timing to encryption

License

This project is developed for educational and research purposes.

Authors

BioCryptNet Development Team

Acknowledgments

• Face recognition capabilities powered by face_recognition library
• Deep learning framework: PyTorch
• Web framework: FastAPI
• Frontend: React + Vite

Submission Deliverables

1. ✅ Project Source Code: Complete backend and frontend implementation
2. ✅ Run Instructions: Detailed setup and execution guide (this README)
3. ✅ Architecture Documentation: Technical details and system design
4. ✅ Security Analysis: Metrics, findings, and lessons learned

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Note: This is a research prototype. For production deployment, additional security hardening and testing are essential.