dipanjanS/adversarial-learning-robustness
Contains materials for workshops pertaining to adversarial robustness in deep learning.
This project provides educational materials and runnable Jupyter notebooks to understand and implement adversarial robustness in deep learning models. It takes basic deep learning knowledge and demonstrates how to make models more resistant to adversarial attacks. The primary user is a deep learning researcher or practitioner looking to enhance their model security.
No commits in the last 6 months.
Use this if you are a deep learning practitioner interested in learning about and experimenting with techniques to make your models robust against adversarial attacks.
Not ideal if you need a production-ready solution for immediate deployment or a comprehensive, ready-to-use library for model hardening without prior deep learning knowledge.
Stars
86
Forks
39
Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Apr 06, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/dipanjanS/adversarial-learning-robustness"
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