Metadata-Version: 2.1
Name: Adversarial Robustness Toolbox
Version: 0.3.0
Summary: IBM Adversarial machine learning toolbox
Home-page: https://github.com/IBM/adversarial-robustness-toolbox
Author: Irina Nicolae
Author-email: maria-irina.nicolae@ibm.com
License: MIT
Description: # Adversarial Robustness Toolbox (ART v0.3.0)
        [![Build Status](https://travis-ci.org/IBM/adversarial-robustness-toolbox.svg?branch=master)](https://travis-ci.org/IBM/adversarial-robustness-toolbox) [![Documentation Status](https://readthedocs.org/projects/adversarial-robustness-toolbox/badge/?version=latest)](http://adversarial-robustness-toolbox.readthedocs.io/en/latest/?badge=latest) [![GitHub version](https://badge.fury.io/gh/IBM%2Fadversarial-robustness-toolbox.svg)](https://badge.fury.io/gh/IBM%2Fadversarial-robustness-toolbox)
        
        This is a library dedicated to **adversarial machine learning**. Its purpose is to allow rapid crafting and analysis of attacks and defense methods for machine learning models. The Adversarial Robustness Toolbox provides an implementation for many state-of-the-art methods for attacking and defending classifiers.
        
        The library is still under development. Feedback, bug reports and extensions are highly appreciated. Get in touch with us on [Slack](https://ibm-art.slack.com) (invite [here](https://join.slack.com/t/ibm-art/shared_invite/enQtMzkyOTkyODE4NzM4LTlkMWY3MzgyZDA4ZDdiNzUzY2NhMjc5YmFhZTYzZGYwNDM4YTE1ODhhNDYyNmFlMGFjNWY4ODgyM2EwYTFjYTc))!
        
        ## Supported attack and defense methods
        
        The library contains implementations of the following attacks:
        * DeepFool ([Moosavi-Dezfooli et al., 2015](https://arxiv.org/abs/1511.04599))
        * Fast Gradient Method ([Goodfellow et al., 2014](https://arxiv.org/abs/1412.6572))
        * Basic Iterative Method ([Kurakin et al., 2016](https://arxiv.org/abs/1607.02533))
        * Jacobian Saliency Map ([Papernot et al., 2016](https://arxiv.org/abs/1511.07528))
        * Universal Perturbation ([Moosavi-Dezfooli et al., 2016](https://arxiv.org/abs/1610.08401))
        * Virtual Adversarial Method ([Miyato et al., 2015](https://arxiv.org/abs/1507.00677))
        * C&amp;W Attack ([Carlini and Wagner, 2016](https://arxiv.org/abs/1608.04644))
        * NewtonFool ([Jang et al., 2017](http://doi.acm.org/10.1145/3134600.3134635))
        
        The following defense methods are also supported:
        * Feature squeezing ([Xu et al., 2017](http://arxiv.org/abs/1704.01155))
        * Spatial smoothing ([Xu et al., 2017](http://arxiv.org/abs/1704.01155))
        * Label smoothing ([Warde-Farley and Goodfellow, 2016](https://pdfs.semanticscholar.org/b5ec/486044c6218dd41b17d8bba502b32a12b91a.pdf))
        * Adversarial training ([Szegedy et al., 2013](http://arxiv.org/abs/1312.6199))
        * Virtual adversarial training ([Miyato et al., 2015](https://arxiv.org/abs/1507.00677))
        * Gaussian data augmentation ([Zantedeschi et al., 2017](https://arxiv.org/abs/1707.06728))
        
        ## Setup
        
        ### Installation with `pip`
        
        The toolbox is designed to run with Python 2 and 3.
        The library can be installed from the PyPi repository using `pip`:
        
        ```bash
        pip install adversarial-robustness-toolbox
        ```
        
        ### Manual installation
        
        For the most recent version of the library, either download the source code or clone the repository in your directory of choice:
        
        ```bash
        git clone https://github.com/IBM/adversarial-robustness-toolbox
        ```
        
        To install ART, do the following in the project folder:
        ```bash
        pip install .
        ```
        
        The library comes with a basic set of unit tests. To check your install, you can run all the unit tests by calling the test script in the install folder:
        
        ```bash
        bash run_tests.sh
        ```
        
        ## Running ART
        
        Some examples of how to use ART when writing your own code can be found in the `examples` folder. See `examples/README.md` for more information about what each example does. To run an example, use the following command:
        ```bash
        python examples/<example_name>.py
        ```
        
        The `notebooks` folder contains Jupyter notebooks with detailed walkthroughs of some usage scenarios. 
        
        ## Citing ART
        
        If you use ART for research, please consider citing the following reference paper:
        ```
        @article{art2018,
            title = {Adversarial Robustness Toolbox v0.3.0},
            author = {Nicolae, Maria-Irina and Sinn, Mathieu and Tran, Minh~Ngoc and Rawat, Ambrish and Wistuba, Martin and Zantedeschi, Valentina and Baracaldo, Nathalie and Chen, Bryant and Ludwig, Heiko and Molloy, Ian and Edwards, Ben},
            journal = {CoRR},
            volume = {1807.01069}
            year = {2018},
            url = {https://arxiv.org/pdf/1807.01069}
        }
        ```
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Description-Content-Type: text/markdown
Provides-Extra: tests
