FGSM Adversarial Attack
Adversarial machine learning demo deployed across local and cloud environments
Overview
Fast Gradient Sign Method adversarial attacks on ML models. Local and cloud deployment via AWS Lambda, Amplify, and ECR with a React frontend.
Problem
Security risks in machine learning are often explained academically, but developers rarely get a usable demo that shows how adversarial attacks behave in practice and how to expose them safely.
Solution
This project packages FGSM attack logic into a deployable product with an API layer, frontend, and cloud infrastructure so the concept becomes tangible.
Impact
It makes adversarial ML easier to teach, test, and demonstrate while also showing deployment discipline beyond a notebook environment.
Stack
Built with a practical stack
The stack was chosen around the practical shape of the build: what needed to run in production, what needed to stay readable, and what made iteration faster.