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

• 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

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

• 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

• 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)

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

cd frontend
npm install
npm run dev

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

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:

• Frontend: http://localhost:3000
• Backend API: http://localhost:5000

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

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

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

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

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

# Frontend tests
cd frontend
npm test

# Backend tests
cd backend
python -m pytest

• 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)

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

This project is created for educational and hackathon purposes.