Notreal.ly

Deepfake Detection Platform

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NotReal.ly - Deepfake Detection Platform

A comprehensive deepfake detection system built for hackathon demonstration, featuring a Next.js frontend and Flask backend with multi-modal AI analysis.

Test at https://notreally.vercel.app/

Features

  • Multi-Modal Analysis: Combines facial feature analysis, audio processing, and metadata extraction
  • Real-time Processing: Fast analysis with live progress updates
  • Interactive Dashboard: Beautiful charts and detailed feature breakdowns
  • Explainable AI: Shows which features contributed to the authenticity score
  • Modern UI: Built with Next.js, TypeScript, and Tailwind CSS

Architecture

wit-hackathon/
├── frontend/          # Next.js React application
│   ├── src/
│   │   ├── app/       # App router pages
│   │   ├── components/ # React components
│   │   └── types/     # TypeScript type definitions
│   └── package.json
├── backend/           # Flask Python API
│   ├── app.py         # Main Flask application
│   ├── requirements.txt
│   └── uploads/       # Temporary file storage
└── plan.md           # Detailed implementation plan

Tech Stack

Frontend

  • Next.js 14 with App Router
  • TypeScript for type safety
  • Tailwind CSS for styling
  • Recharts for data visualization
  • Lucide React for icons
  • Axios for API communication

Backend

  • Flask for REST API
  • Flask-CORS for cross-origin requests
  • OpenCV for video processing
  • MediaPipe for facial analysis
  • Librosa for audio processing
  • XGBoost for ML classification
  • SQLite for data storage (planned)

🚀 Quick Start

Prerequisites

  • Node.js 18+ and npm
  • Python 3.8+
  • FFmpeg (for video processing)

Frontend Setup

cd frontend
npm install
npm run dev

Backend Setup

cd backend
pip install -r requirements.txt
python app.py

Train the model (using your real/fake datasets)

cd backend
# Quotes are needed for the space in the real dataset folder name
python train_model.py "DFD_original sequences" DFD_manipulated_sequences
# This writes backend/model.pkl

The application will be available at:

📊 Analysis Pipeline

  1. File Upload: User uploads video file via drag-and-drop interface
  2. Feature Extraction:
    • Facial landmark detection using MediaPipe
    • Blink rate calculation
    • Facial jitter analysis
    • Audio MFCC extraction
  3. ML Classification: XGBoost model predicts authenticity score
  4. Results Display: Interactive dashboard with charts and explanations

🎯 Key Features

File Upload Component

  • Drag-and-drop interface
  • File type and size validation
  • Real-time upload progress
  • Error handling

Analysis Dashboard

  • Authenticity score with confidence level
  • Feature breakdown charts
  • Detailed metrics table
  • Explainable AI insights

API Endpoints

  • POST /api/analyze - Upload and analyze video
  • GET /api/results/<job_id> - Get analysis results
  • GET /api/health - Health check

🔧 Development

Adding New Features

  1. Update types in frontend/src/types/
  2. Create components in frontend/src/components/
  3. Add API endpoints in backend/app.py
  4. Update the analysis pipeline as needed

Testing

# Frontend tests
cd frontend
npm test

# Backend tests
cd backend
python -m pytest

📈 Future Enhancements

  • [ ] Real-time video streaming analysis
  • [ ] Batch processing for multiple files
  • [ ] User authentication and history
  • [ ] Advanced ML models (CNN, Transformer)
  • [ ] API rate limiting and security
  • [ ] Docker containerization
  • [ ] Cloud deployment (AWS/GCP)

🤝 Contributing

This is a hackathon project, but contributions are welcome! Please:

  1. Fork the repository
  2. Create a feature branch
  3. Make your changes
  4. Submit a pull request

📄 License

This project is created for educational and hackathon purposes.

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