Deepfake Detection Web Application

A web-based AI-powered application for detecting deepfake content in images and videos.

Project Structure

├── frontend/                 # React TypeScript frontend
│   ├── src/
│   │   ├── components/      # React components
│   │   ├── services/        # API services
│   │   └── types/           # TypeScript type definitions
│   └── public/              # Static assets
├── backend/                  # FastAPI Python backend
│   ├── app/
│   │   ├── api/            # API endpoints
│   │   ├── services/       # Business logic services
│   │   ├── models/         # Data models
│   │   └── core/           # Core configuration
│   └── tests/              # Backend tests
├── shared/                   # Shared utilities and types
│   ├── utils/              # Common utilities
│   └── types/              # Shared type definitions
└── .github/workflows/        # CI/CD configuration

Prerequisites

• Docker and Docker Compose
• Node.js 18+ (for local development)
• Python 3.11+ (for local development)

Quick Start with Docker

1. Clone the repository
2. Run the application:

   docker-compose up --build

3. Access the application:
   • Frontend: http://localhost:3000
   • Backend API: http://localhost:8000
   • API Documentation: http://localhost:8000/docs

Development Setup

Backend Development

1. Navigate to the backend directory:

   cd backend

2. Create a virtual environment:

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

3. Install dependencies:

   pip install -r requirements.txt

4. Run the development server:

   uvicorn app.main:app --reload

Frontend Development

1. Navigate to the frontend directory:

   cd frontend

2. Install dependencies:

   npm install

3. Start the development server:

   npm start

Development with Docker

For development with hot reloading:

docker-compose -f docker-compose.yml -f docker-compose.dev.yml up --build

Testing

Backend Tests

cd backend
pytest tests/ -v

Frontend Tests

cd frontend
npm test

Features

• Upload and analyze images for deepfake detection
• Upload and analyze videos with frame-by-frame analysis
• AI-powered detection using ensemble models
• Detailed analysis reports with confidence scores
• Secure file handling with automatic cleanup
• Real-time progress tracking
• Responsive web interface

Technology Stack

Frontend:

• React 18 with TypeScript
• Material-UI for components
• React Query for state management
• Axios for API communication

Backend:

• FastAPI for REST API
• Celery for background processing
• Redis for task queue
• PyTorch/TensorFlow for AI models
• OpenCV for video processing

Infrastructure:

• Docker for containerization
• GitHub Actions for CI/CD
• Nginx for reverse proxy

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Run the test suite
6. Submit a pull request

License

This project is licensed under the MIT License.