softsys4ai/athena
Athena: A Framework for Defending Machine Learning Systems Against Adversarial Attacks
Safeguard your critical machine learning systems, like those in self-driving cars or medical image analysis, from malicious attacks. This tool takes your existing machine learning model and processes its inputs through multiple 'weak defenses' to produce a more robust, attack-resistant model. It's designed for machine learning engineers and researchers responsible for deploying secure and reliable AI.
No commits in the last 6 months.
Use this if you need to build a machine learning model that is highly resistant to adversarial examples, which are subtle changes to inputs designed to trick your model.
Not ideal if you're looking for a simple, off-the-shelf defense technique, as this is a framework for constructing custom defenses.
Stars
44
Forks
9
Language
Python
License
MIT
Category
Last pushed
Sep 23, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/softsys4ai/athena"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
QData/TextAttack
TextAttack 🐙 is a Python framework for adversarial attacks, data augmentation, and model...
ebagdasa/backdoors101
Backdoors Framework for Deep Learning and Federated Learning. A light-weight tool to conduct...
THUYimingLi/backdoor-learning-resources
A list of backdoor learning resources
zhangzp9970/MIA
Unofficial pytorch implementation of paper: Model Inversion Attacks that Exploit Confidence...
LukasStruppek/Plug-and-Play-Attacks
[ICML 2022 / ICLR 2024] Source code for our papers "Plug & Play Attacks: Towards Robust and...