From 7586fb58acf6fb6db77261712b736d2fd799813c Mon Sep 17 00:00:00 2001 From: CoprDistGit Date: Wed, 12 Apr 2023 07:03:03 +0000 Subject: automatic import of python-adversarial-robustness-toolbox --- .gitignore | 1 + python-adversarial-robustness-toolbox.spec | 387 +++++++++++++++++++++++++++++ sources | 1 + 3 files changed, 389 insertions(+) create mode 100644 python-adversarial-robustness-toolbox.spec create mode 100644 sources diff --git a/.gitignore b/.gitignore index e69de29..e62ffcc 100644 --- a/.gitignore +++ b/.gitignore @@ -0,0 +1 @@ +/adversarial-robustness-toolbox-1.14.0.tar.gz diff --git a/python-adversarial-robustness-toolbox.spec b/python-adversarial-robustness-toolbox.spec new file mode 100644 index 0000000..5db7e83 --- /dev/null +++ b/python-adversarial-robustness-toolbox.spec @@ -0,0 +1,387 @@ +%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 +

+ +

+
+ +![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) + +

+ LF AI & Data +

+ +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 + +

+ + +

+
+ +## ART for Red and Blue Teams (selection) + +

+ +

+
+ +## Learn more + +| **[Get Started][get-started]** | **[Documentation][documentation]** | **[Contributing][contributing]** | +|-------------------------------------|-------------------------------|-----------------------------------| +| - [Installation][installation]
- [Examples](examples/README.md)
- [Notebooks](notebooks/README.md) | - [Attacks][attacks]
- [Defences][defences]
- [Estimators][estimators]
- [Metrics][metrics]
- [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)
- [Contributing](CONTRIBUTING.md)
- [Roadmap][roadmap]
- [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 +

+ +

+
+ +![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) + +

+ LF AI & Data +

+ +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 + +

+ + +

+
+ +## ART for Red and Blue Teams (selection) + +

+ +

+
+ +## Learn more + +| **[Get Started][get-started]** | **[Documentation][documentation]** | **[Contributing][contributing]** | +|-------------------------------------|-------------------------------|-----------------------------------| +| - [Installation][installation]
- [Examples](examples/README.md)
- [Notebooks](notebooks/README.md) | - [Attacks][attacks]
- [Defences][defences]
- [Estimators][estimators]
- [Metrics][metrics]
- [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)
- [Contributing](CONTRIBUTING.md)
- [Roadmap][roadmap]
- [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 +

+ +

+
+ +![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) + +

+ LF AI & Data +

+ +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 + +

+ + +

+
+ +## ART for Red and Blue Teams (selection) + +

+ +

+
+ +## Learn more + +| **[Get Started][get-started]** | **[Documentation][documentation]** | **[Contributing][contributing]** | +|-------------------------------------|-------------------------------|-----------------------------------| +| - [Installation][installation]
- [Examples](examples/README.md)
- [Notebooks](notebooks/README.md) | - [Attacks][attacks]
- [Defences][defences]
- [Estimators][estimators]
- [Metrics][metrics]
- [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)
- [Contributing](CONTRIBUTING.md)
- [Roadmap][roadmap]
- [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 - 1.14.0-1 +- Package Spec generated diff --git a/sources b/sources new file mode 100644 index 0000000..c5cc888 --- /dev/null +++ b/sources @@ -0,0 +1 @@ +6d042387dabff68a649703955ca01e71 adversarial-robustness-toolbox-1.14.0.tar.gz -- cgit v1.2.3