ChandlerBang/awesome-graph-attack-papers
Adversarial attacks and defenses on Graph Neural Networks.
This resource provides a curated collection of research papers focused on how machine learning models that analyze interconnected data (like social networks or molecular structures) can be intentionally misled, and how to protect them from such attacks. It compiles a comprehensive list of studies detailing various methods to 'trick' these models and techniques to make them more resilient. Researchers and practitioners working with graph-structured data and GNNs will find this useful for understanding security vulnerabilities and developing robust systems.
391 stars. No commits in the last 6 months.
Use this if you are developing, deploying, or researching machine learning models that operate on graph data and need to understand their vulnerabilities to adversarial attacks or how to defend against them.
Not ideal if you are looking for an introductory guide to graph neural networks or general machine learning security without a specific focus on graph-structured data.
Stars
391
Forks
32
Language
—
License
CC0-1.0
Category
Last pushed
Feb 22, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/ChandlerBang/awesome-graph-attack-papers"
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
DSE-MSU/DeepRobust
A pytorch adversarial library for attack and defense methods on images and graphs
cleverhans-lab/cleverhans
An adversarial example library for constructing attacks, building defenses, and benchmarking both
BorealisAI/advertorch
A Toolbox for Adversarial Robustness Research