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Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams

adversarial-robustness-toolbox.readthedocs.io/en/latest/

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MIT license

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Name		Name	Last commit message	Last commit date
Latest commit History 13,045 Commits
.github		.github
art		art
docs		docs
examples		examples
notebooks		notebooks
tests		tests
utils		utils
.coveragerc		.coveragerc
.dockerIgnore		.dockerIgnore
.gitattributes		.gitattributes
.gitignore		.gitignore
.pylintrc		.pylintrc
AUTHORS		AUTHORS
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
Dockerfile		Dockerfile
LICENSE		LICENSE
MAINTAINERS.md		MAINTAINERS.md
MANIFEST.in		MANIFEST.in
Makefile		Makefile
PULL_REQUEST_TEMPLATE.md		PULL_REQUEST_TEMPLATE.md
README-cn.md		README-cn.md
README.md		README.md
SECURITY.md		SECURITY.md
codecov.yml		codecov.yml
conftest.py		conftest.py
pyproject.toml		pyproject.toml
readthedocs.yml		readthedocs.yml
requirements_test.txt		requirements_test.txt
run_tests.sh		run_tests.sh
setup.cfg		setup.cfg
setup.py		setup.py

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Adversarial Robustness Toolbox (ART) v1.20

中文README请按此处

Adversarial Robustness Toolbox (ART) is a Python library for Machine Learning Security. ART is hosted by theLinux Foundation AI & Data Foundation (LF AI & Data). ART provides tools that enabledevelopers and researchers to defend and evaluate Machine Learning models and applications against theadversarial threats of Evasion, Poisoning, Extraction, and Inference. ART supports all popular machine learning frameworks(TensorFlow, Keras, PyTorch, scikit-learn, XGBoost, LightGBM, CatBoost, GPy, etc.), all data types(images, tables, audio, video, etc.) and machine learning tasks (classification, object detection, speech recognition,generation, certification, etc.).

Adversarial Threats

ART for Red and Blue Teams (selection)

Learn more

Get Started	Documentation	Contributing
-Installation -Examples -Notebooks	-Attacks -Defences -Estimators -Metrics -Technical Documentation	-Slack,Invitation -Contributing -Roadmap -Citing

The library is under continuous development. Feedback, bug reports and contributions are very welcome!

Acknowledgment

This material is partially based upon work supported by the Defense Advanced Research Projects Agency (DARPA) underContract No. HR001120C0013. Any opinions, findings and conclusions or recommendations expressed in this material arethose of the author(s) and do not necessarily reflect the views of the Defense Advanced Research Projects Agency (DARPA).