# Adversarial Robustness Toolbox (ART) v1.20
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[中文README请按此处](README-cn.md)

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Adversarial Robustness Toolbox (ART) is a Python library for Machine Learning Security. ART is hosted by the 
[Linux Foundation AI & Data Foundation](https://lfaidata.foundation) (LF AI & Data). ART provides tools that enable
developers and researchers to defend and evaluate Machine Learning models and applications against the
adversarial 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

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## ART for Red and Blue Teams (selection)

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## Learn more

| **[Get Started][get-started]**     | **[Documentation][documentation]**     | **[Contributing][contributing]**           |
|-------------------------------------|-------------------------------|-----------------------------------|
| - [Installation][installation]<br>- [Examples](examples/README.md)<br>- [Notebooks](notebooks/README.md) | - [Attacks][attacks]<br>- [Defences][defences]<br>- [Estimators][estimators]<br>- [Metrics][metrics]<br>- [Technical Documentation](https://adversarial-robustness-toolbox.readthedocs.io) | - [Slack](https://ibm-art.slack.com), [Invitation](https://join.slack.com/t/ibm-art/shared_invite/enQtMzkyOTkyODE4NzM4LTA4NGQ1OTMxMzFmY2Q1MzE1NWI2MmEzN2FjNGNjOGVlODVkZDE0MjA1NTA4OGVkMjVkNmQ4MTY1NmMyOGM5YTg)<br>- [Contributing](CONTRIBUTING.md)<br>- [Roadmap][roadmap]<br>- [Citing][citing] |

[get-started]: https://github.com/Trusted-AI/adversarial-robustness-toolbox/wiki/Get-Started
[attacks]: https://github.com/Trusted-AI/adversarial-robustness-toolbox/wiki/ART-Attacks
[defences]: https://github.com/Trusted-AI/adversarial-robustness-toolbox/wiki/ART-Defences
[estimators]: https://github.com/Trusted-AI/adversarial-robustness-toolbox/wiki/ART-Estimators
[metrics]: https://github.com/Trusted-AI/adversarial-robustness-toolbox/wiki/ART-Metrics
[contributing]: https://github.com/Trusted-AI/adversarial-robustness-toolbox/wiki/Contributing
[documentation]: https://github.com/Trusted-AI/adversarial-robustness-toolbox/wiki/Documentation
[installation]: https://github.com/Trusted-AI/adversarial-robustness-toolbox/wiki/Get-Started#setup
[roadmap]: https://github.com/Trusted-AI/adversarial-robustness-toolbox/wiki/Roadmap
[citing]: https://github.com/Trusted-AI/adversarial-robustness-toolbox/wiki/Contributing#citing-art

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) under
Contract No. HR001120C0013. Any opinions, findings and conclusions or recommendations expressed in this material are
those of the author(s) and do not necessarily reflect the views of the Defense Advanced Research Projects Agency (DARPA).