Deepfake Detector

A comprehensive deepfake detection system with FastAPI backend, React frontend, and ML model.

Project Structure

├── backend/     # FastAPI Backend API
├── model/       # Machine Learning Model
├── UI/          # React Frontend
└── README.md    # This file

Backend Setup

1. cd backend
2. python3.12 -m venv venv (or python3.11)
3. source venv/bin/activate
4. pip install -r requirements.txt
5. cp env.example .env
6. Create database: createdb deepfake_detector (or use psql command)
7. alembic upgrade head
8. docker-compose up -d OR uvicorn app.main:app --reload

Migrations:

• Generate: alembic revision --autogenerate -m "description"
• Apply: alembic upgrade head
• Rollback: alembic downgrade -1

API Docs:

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

Frontend Setup

1. cd UI
2. npm install
3. npm start

Visit http://localhost:5173 (Vite default port)

Scripts:

• npm start - Development server
• npm run build - Production build
• npm run test:unit - Run tests

Tech Stack: React 18 • TypeScript • Vite • Tailwind CSS

ML Model

• Uses Xception model for deepfake detection
• OpenCV/MediaPipe for face detection
• Processes videos frame-by-frame
• Returns confidence scores and predictions

Key Features

• JWT authentication
• Video/image upload and analysis
• Real-time processing with progress tracking
• Confidence scoring with detailed reports
• PostgreSQL database with SQLModel/SQLAlchemy