Flux

Flux is a distributed, spoof-resistant facial authentication service.

• Distributed: Built on peer-to-peer protocols like WebRTC and WebSocket with a scalable microservice architecture.
• Spoof-resistant: Detects face spoofs and deepfakes using camera physics and a regression model, as described in this paper.
• Accurate Authentication: Uses the VGGNet convolutional neural network and a vector database to identify individuals.
• Hardware-agnostic: Works on any device with a camera and screen—no specialized hardware needed.

Components

1. Frontend: Next.js website which implements color flashing and video streaming.
2. Server: Go server which sends control commands to frontend, processes video streams and manages the vector database.
3. Inference: Python backend deploying computer vision models for liveness detection and convolutional neural networks for facial recognition.
4. Training: Jupyter notebooks for training liveness detection regression model.

Installation

To start backend services, place a model.joblib file at /inference/model.joblib, then run:

docker compose up --build

To start frontend services, ensure that you are on Node 18, then run:

cd frontend
npm i
npm run dev

Model Training

Note that this repository does not come with a pre-existing dataset or model due to privacy concerns. To train the regression model using your data, please use the Jupyter notebooks in /training. Training data can be generated using the frontend module by recording videos of different faces in front of the flashing color bands.