BorealisAI/advertorch
A Toolbox for Adversarial Robustness Research
This tool helps machine learning researchers evaluate and improve the security of their deep learning models against adversarial attacks. It takes an existing PyTorch model and data, and then generates 'adversarial examples'—slightly modified inputs designed to fool the model—or applies defenses to make the model more robust. It's for researchers focused on making AI systems more reliable and trustworthy in the face of malicious inputs.
1,367 stars. Used by 1 other package. No commits in the last 6 months. Available on PyPI.
Use this if you are a machine learning researcher working with PyTorch and need to test your model's vulnerability to adversarial attacks or develop defenses against them.
Not ideal if you are a practitioner looking for a general-purpose machine learning library or if your models are not built with PyTorch.
Stars
1,367
Forks
201
Language
Jupyter Notebook
License
LGPL-3.0
Category
Last pushed
Sep 14, 2023
Commits (30d)
0
Reverse dependents
1
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