🧠 Deepfake Detection on Face-Swap Based Videos

A complete end-to-end AI/ML project to detect face-swapped deepfake videos using XceptionNet. Built with a scalable and production-ready architecture including a modern frontend, robust backend, containerized deployment, and observability stack.

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🔍 Overview

Deepfakes pose a significant threat to digital authenticity. This project tackles the challenge by building a high-accuracy classifier to detect face-swapped deepfake videos using a convolutional neural network.

• Model: XceptionNet (CNN)
• Dataset: FaceForensics++
• Frontend: Vite + React
• Backend: FastAPI
• Infrastructure: Docker, Docker Compose, NGINX
• Monitoring: Prometheus + Grafana

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📁 Folder Structure

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├── project/                     # FastAPI server
├── vision-truth-finder/         # React (Vite) client
├── test/                        # Test image: REAL and video:Fake
├── project/model/               # XceptionNet model
├── project/docker-compose.yml   # Docker orchestration
├── project/Dockerfile           # Backend Dockerfile
├── project/nginx/               # NGINX reverse proxy config
├── project/prometheus/          # Prometheus config
├── project/.env                 # Environment variables
├── README.md
├── .gitignore
├── LICENSE
└── run.sh 

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🛠️ Features

🔬 Deep Learning Model

• Trained a deepfake detection classifier using XceptionNet
• Preprocessed ~42,000 frames from the FaceForensics++ dataset
• Achieved 91.76% accuracy on test set
• Trained on 2×NVIDIA T4 GPUs with training time under 3 hours

⚙️ Backend (FastAPI)

• Exposes a /predict endpoint for model inference
• Handles video frame input and passes through preprocessing pipeline
• Uses python-dotenv to load environment variables securely
• Structured logging for debugging and traceability

🖼️ Frontend (React + Vite)

• Built a lightweight UI for uploading video frames or images
• Shows real-time prediction results and model confidence
• Connected directly to FastAPI via REST

🐳 Dockerized Architecture

• Backend and frontend containerized with Docker
• All services orchestrated using Docker Compose
• .env support for flexible configuration

🌐 NGINX Reverse Proxy

• Serves as the single entrypoint to all services
• Handles CORS, header injection, and internal routing
• Access controlled to internal Docker network

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🚀 Getting Started

1. Clone the Repository

git clone https://github.com/argodevops/deepfake-detection

2. Build and Run All Services

./run.sh start

3. Access the Services

• Frontend: http://localhost:8080
• Backend: http://localhost:8000/docs

4. Stop all Services

./run.sh stop

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📦 Dataset Info

• Used FaceForensics++ (face-swap subset)
• Preprocessed video files into image frames
• Split into train/test (balanced real/fake)

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📉 Model Performance

Metric	Value
Accuracy	91.76%
Training Time	~2 hours
Hardware	2×T4 GPUs

The model can be downloaded from Kaggle

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📝 License

This project is for educational and research purposes only.