%global _empty_manifest_terminate_build 0
Name:		python-adversarial-robustness-toolbox
Version:	1.14.0
Release:	1
Summary:	Toolbox for adversarial machine learning.
License:	MIT
URL:		https://github.com/Trusted-AI/adversarial-robustness-toolbox
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/32/52/98469e81703162447154cdd9f2270e4f8ecc39ad6159e917c0767fad4937/adversarial-robustness-toolbox-1.14.0.tar.gz
BuildArch:	noarch

Requires:	python3-numpy
Requires:	python3-scipy
Requires:	python3-scikit-learn
Requires:	python3-six
Requires:	python3-setuptools
Requires:	python3-tqdm
Requires:	python3-mxnet
Requires:	python3-catboost
Requires:	python3-lightgbm
Requires:	python3-tensorflow
Requires:	python3-tensorflow-addons
Requires:	python3-h5py
Requires:	python3-torch
Requires:	python3-torchvision
Requires:	python3-xgboost
Requires:	python3-pandas
Requires:	python3-kornia
Requires:	python3-matplotlib
Requires:	python3-Pillow
Requires:	python3-statsmodels
Requires:	python3-pydub
Requires:	python3-resampy
Requires:	python3-ffmpeg-python
Requires:	python3-cma
Requires:	python3-librosa
Requires:	python3-opencv-python
Requires:	python3-numba
Requires:	python3-catboost
Requires:	python3-sphinx
Requires:	python3-sphinx-rtd-theme
Requires:	python3-sphinx-autodoc-annotation
Requires:	python3-sphinx-autodoc-typehints
Requires:	python3-matplotlib
Requires:	python3-numpy
Requires:	python3-scipy
Requires:	python3-six
Requires:	python3-scikit-learn
Requires:	python3-Pillow
Requires:	python3-GPy
Requires:	python3-keras
Requires:	python3-h5py
Requires:	python3-lightgbm
Requires:	python3-tensorflow-gpu
Requires:	python3-lingvo
Requires:	python3-pydub
Requires:	python3-resampy
Requires:	python3-librosa
Requires:	python3-mxnet
Requires:	python3-matplotlib
Requires:	python3-Pillow
Requires:	python3-statsmodels
Requires:	python3-pydub
Requires:	python3-resampy
Requires:	python3-ffmpeg-python
Requires:	python3-cma
Requires:	python3-pandas
Requires:	python3-librosa
Requires:	python3-opencv-python
Requires:	python3-pytest
Requires:	python3-pytest-flake8
Requires:	python3-pytest-mock
Requires:	python3-pytest-cov
Requires:	python3-codecov
Requires:	python3-requests
Requires:	python3-sortedcontainers
Requires:	python3-numba
Requires:	python3-torch
Requires:	python3-torchvision
Requires:	python3-torch
Requires:	python3-torchvision
Requires:	python3-torchaudio
Requires:	python3-pydub
Requires:	python3-resampy
Requires:	python3-librosa
Requires:	python3-torch
Requires:	python3-torchvision
Requires:	python3-kornia
Requires:	python3-Pillow
Requires:	python3-ffmpeg-python
Requires:	python3-opencv-python
Requires:	python3-tensorflow
Requires:	python3-tensorflow-addons
Requires:	python3-h5py
Requires:	python3-tensorflow
Requires:	python3-tensorflow-addons
Requires:	python3-h5py
Requires:	python3-pydub
Requires:	python3-resampy
Requires:	python3-librosa
Requires:	python3-tensorflow
Requires:	python3-tensorflow-addons
Requires:	python3-h5py
Requires:	python3-Pillow
Requires:	python3-ffmpeg-python
Requires:	python3-opencv-python
Requires:	python3-xgboost

%description
# Adversarial Robustness Toolbox (ART) v1.14
<p align="center">
  <img src="docs/images/art_lfai.png?raw=true" width="467" title="ART logo">
</p>
<br />

![Continuous Integration](https://github.com/Trusted-AI/adversarial-robustness-toolbox/workflows/Continuous%20Integration/badge.svg)
![CodeQL](https://github.com/Trusted-AI/adversarial-robustness-toolbox/workflows/CodeQL/badge.svg)
[![Documentation Status](https://readthedocs.org/projects/adversarial-robustness-toolbox/badge/?version=latest)](http://adversarial-robustness-toolbox.readthedocs.io/en/latest/?badge=latest)
[![PyPI](https://badge.fury.io/py/adversarial-robustness-toolbox.svg)](https://badge.fury.io/py/adversarial-robustness-toolbox)
[![codecov](https://codecov.io/gh/Trusted-AI/adversarial-robustness-toolbox/branch/main/graph/badge.svg)](https://codecov.io/gh/Trusted-AI/adversarial-robustness-toolbox)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/adversarial-robustness-toolbox)](https://pypi.org/project/adversarial-robustness-toolbox/)
[![slack-img](https://img.shields.io/badge/chat-on%20slack-yellow.svg)](https://ibm-art.slack.com/)
[![Downloads](https://pepy.tech/badge/adversarial-robustness-toolbox)](https://pepy.tech/project/adversarial-robustness-toolbox)
[![Downloads](https://pepy.tech/badge/adversarial-robustness-toolbox/month)](https://pepy.tech/project/adversarial-robustness-toolbox)
[![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/5090/badge)](https://bestpractices.coreinfrastructure.org/projects/5090)

[中文README请按此处](README-cn.md)

<p align="center">
  <img src="https://raw.githubusercontent.com/lfai/artwork/master/lfaidata-assets/lfaidata-project-badge/graduate/color/lfaidata-project-badge-graduate-color.png" alt="LF AI & Data" width="300"/>
</p>

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, MXNet, 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

<p align="center">
  <img src="docs/images/adversarial_threats_attacker.png?raw=true" width="400" title="ART logo">
  <img src="docs/images/adversarial_threats_art.png?raw=true" width="400" title="ART logo">
</p>
<br />

## ART for Red and Blue Teams (selection)

<p align="center">
  <img src="docs/images/white_hat_blue_red.png?raw=true" width="800" title="ART Red and Blue Teams">
</p>
<br />

## 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).




%package -n python3-adversarial-robustness-toolbox
Summary:	Toolbox for adversarial machine learning.
Provides:	python-adversarial-robustness-toolbox
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-adversarial-robustness-toolbox
# Adversarial Robustness Toolbox (ART) v1.14
<p align="center">
  <img src="docs/images/art_lfai.png?raw=true" width="467" title="ART logo">
</p>
<br />

![Continuous Integration](https://github.com/Trusted-AI/adversarial-robustness-toolbox/workflows/Continuous%20Integration/badge.svg)
![CodeQL](https://github.com/Trusted-AI/adversarial-robustness-toolbox/workflows/CodeQL/badge.svg)
[![Documentation Status](https://readthedocs.org/projects/adversarial-robustness-toolbox/badge/?version=latest)](http://adversarial-robustness-toolbox.readthedocs.io/en/latest/?badge=latest)
[![PyPI](https://badge.fury.io/py/adversarial-robustness-toolbox.svg)](https://badge.fury.io/py/adversarial-robustness-toolbox)
[![codecov](https://codecov.io/gh/Trusted-AI/adversarial-robustness-toolbox/branch/main/graph/badge.svg)](https://codecov.io/gh/Trusted-AI/adversarial-robustness-toolbox)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/adversarial-robustness-toolbox)](https://pypi.org/project/adversarial-robustness-toolbox/)
[![slack-img](https://img.shields.io/badge/chat-on%20slack-yellow.svg)](https://ibm-art.slack.com/)
[![Downloads](https://pepy.tech/badge/adversarial-robustness-toolbox)](https://pepy.tech/project/adversarial-robustness-toolbox)
[![Downloads](https://pepy.tech/badge/adversarial-robustness-toolbox/month)](https://pepy.tech/project/adversarial-robustness-toolbox)
[![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/5090/badge)](https://bestpractices.coreinfrastructure.org/projects/5090)

[中文README请按此处](README-cn.md)

<p align="center">
  <img src="https://raw.githubusercontent.com/lfai/artwork/master/lfaidata-assets/lfaidata-project-badge/graduate/color/lfaidata-project-badge-graduate-color.png" alt="LF AI & Data" width="300"/>
</p>

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, MXNet, 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

<p align="center">
  <img src="docs/images/adversarial_threats_attacker.png?raw=true" width="400" title="ART logo">
  <img src="docs/images/adversarial_threats_art.png?raw=true" width="400" title="ART logo">
</p>
<br />

## ART for Red and Blue Teams (selection)

<p align="center">
  <img src="docs/images/white_hat_blue_red.png?raw=true" width="800" title="ART Red and Blue Teams">
</p>
<br />

## 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).




%package help
Summary:	Development documents and examples for adversarial-robustness-toolbox
Provides:	python3-adversarial-robustness-toolbox-doc
%description help
# Adversarial Robustness Toolbox (ART) v1.14
<p align="center">
  <img src="docs/images/art_lfai.png?raw=true" width="467" title="ART logo">
</p>
<br />

![Continuous Integration](https://github.com/Trusted-AI/adversarial-robustness-toolbox/workflows/Continuous%20Integration/badge.svg)
![CodeQL](https://github.com/Trusted-AI/adversarial-robustness-toolbox/workflows/CodeQL/badge.svg)
[![Documentation Status](https://readthedocs.org/projects/adversarial-robustness-toolbox/badge/?version=latest)](http://adversarial-robustness-toolbox.readthedocs.io/en/latest/?badge=latest)
[![PyPI](https://badge.fury.io/py/adversarial-robustness-toolbox.svg)](https://badge.fury.io/py/adversarial-robustness-toolbox)
[![codecov](https://codecov.io/gh/Trusted-AI/adversarial-robustness-toolbox/branch/main/graph/badge.svg)](https://codecov.io/gh/Trusted-AI/adversarial-robustness-toolbox)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/adversarial-robustness-toolbox)](https://pypi.org/project/adversarial-robustness-toolbox/)
[![slack-img](https://img.shields.io/badge/chat-on%20slack-yellow.svg)](https://ibm-art.slack.com/)
[![Downloads](https://pepy.tech/badge/adversarial-robustness-toolbox)](https://pepy.tech/project/adversarial-robustness-toolbox)
[![Downloads](https://pepy.tech/badge/adversarial-robustness-toolbox/month)](https://pepy.tech/project/adversarial-robustness-toolbox)
[![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/5090/badge)](https://bestpractices.coreinfrastructure.org/projects/5090)

[中文README请按此处](README-cn.md)

<p align="center">
  <img src="https://raw.githubusercontent.com/lfai/artwork/master/lfaidata-assets/lfaidata-project-badge/graduate/color/lfaidata-project-badge-graduate-color.png" alt="LF AI & Data" width="300"/>
</p>

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, MXNet, 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

<p align="center">
  <img src="docs/images/adversarial_threats_attacker.png?raw=true" width="400" title="ART logo">
  <img src="docs/images/adversarial_threats_art.png?raw=true" width="400" title="ART logo">
</p>
<br />

## ART for Red and Blue Teams (selection)

<p align="center">
  <img src="docs/images/white_hat_blue_red.png?raw=true" width="800" title="ART Red and Blue Teams">
</p>
<br />

## 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).




%prep
%autosetup -n adversarial-robustness-toolbox-1.14.0

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-adversarial-robustness-toolbox -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Wed Apr 12 2023 Python_Bot <Python_Bot@openeuler.org> - 1.14.0-1
- Package Spec generated