cudenver-ai/Adversarial-Machine-Learning
Framework for the Adversarial Machine Learning Challenge at CU Denver, showcasing techniques in AI model defense and attack.
This project is a framework designed for the Adversarial Machine Learning Challenge at CU Denver. It provides tools for both attacking and defending AI models, showcasing various techniques to test model robustness. AI researchers and students working on model security would use this to understand and implement adversarial methods.
No commits in the last 6 months.
Use this if you are a developer or AI researcher participating in the Adversarial Machine Learning Challenge or exploring AI model vulnerabilities and defenses.
Not ideal if you are looking for an out-of-the-box solution to secure your production AI models without deep technical understanding.
Stars
9
Forks
1
Language
JavaScript
License
MIT
Category
Last pushed
Nov 08, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/cudenver-ai/Adversarial-Machine-Learning"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
Trusted-AI/adversarial-robustness-toolbox
Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion,...
bethgelab/foolbox
A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX
cleverhans-lab/cleverhans
An adversarial example library for constructing attacks, building defenses, and benchmarking both
DSE-MSU/DeepRobust
A pytorch adversarial library for attack and defense methods on images and graphs
BorealisAI/advertorch
A Toolbox for Adversarial Robustness Research