emaballarin/CARSO
👀🛡️ Code for the paper “Blending adversarial training and representation-conditional purification via aggregation improves adversarial robustness” by Emanuele Ballarin, Alessio Ansuini and Luca Bortolussi (2025)
This project helps researchers and practitioners evaluate and improve the security of their AI models against deliberate attacks. It takes an existing image classification model and training data, and then applies advanced techniques to make the model more resilient, providing a more robust and trustworthy model as an output. This is for anyone building or deploying AI systems where reliability and security against adversarial examples are critical.
No commits in the last 6 months.
Use this if you need to make your image classification models more robust against adversarial attacks that try to trick them.
Not ideal if you are working with non-image data or if your primary concern is model accuracy without considering adversarial threats.
Stars
8
Forks
2
Language
Python
License
MIT
Category
Last pushed
Sep 22, 2025
Commits (30d)
0
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