Adversarial Robustness Toolbox (ART v0.7.0)

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. ART 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 (invite here)!

Supported attacks, defences and metrics

The library contains implementations of the following evasion attacks:

DeepFool (Moosavi-Dezfooli et al., 2015)
Fast gradient method (Goodfellow et al., 2014)
Basic iterative method (Kurakin et al., 2016)
Projected gradient descent (Madry et al., 2017)
Jacobian saliency map (Papernot et al., 2016)
Universal perturbation (Moosavi-Dezfooli et al., 2016)
Virtual adversarial method (Miyato et al., 2015)
C&W L_2 and L_inf attacks (Carlini and Wagner, 2016)
NewtonFool (Jang et al., 2017)
Elastic net attack (Chen et al., 2017)
Spatial transformations attack (Engstrom et al., 2017)
Query-efficient black-box attack (Ilyas et al., 2017)

The following defence methods are also supported:

Feature squeezing (Xu et al., 2017)
Spatial smoothing (Xu et al., 2017)
Label smoothing (Warde-Farley and Goodfellow, 2016)
Adversarial training (Szegedy et al., 2013)
Virtual adversarial training (Miyato et al., 2015)
Gaussian data augmentation (Zantedeschi et al., 2017)
Thermometer encoding (Buckman et al., 2018)
Total variance minimization (Guo et al., 2018)
JPEG compression (Dziugaite et al., 2016)
PixelDefend (Song et al., 2017)

ART also implements detection methods of adversarial samples:

Basic detector based on inputs
Detector trained on the activations of a specific layer

The following detector of poisoning attacks is also supported:

Detector based on activations analysis (Chen et al., 2018)

Robustness metrics:

CLEVER (Weng et al., 2018)
Empirical robustness (Moosavi-Dezfooli et al., 2015)
Loss sensitivity (Arpit et al., 2017)

Setup

Installation with `pip`

The toolbox is designed to run with Python 2 and 3. ART can be installed from the PyPi repository using pip:

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:

git clone https://github.com/IBM/adversarial-robustness-toolbox

To install ART, do the following in the project folder:

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 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:

python examples/<example_name>.py

The notebooks folder contains Jupyter notebooks with detailed walkthroughs of some usage scenarios.

Contributing

Adding new features, improving documentation, fixing bugs, or writing tutorials are all examples of helpful contributions. Furthermore, if you are publishing a new attack or defense, we strongly encourage you to add it to the Adversarial Robustness Toolbox so that others may evaluate it fairly in their own work.

Bug fixes can be initiated through GitHub pull requests. When making code contributions to the Adversarial Robustness Toolbox, we ask that you follow the PEP 8 coding standard and that you provide unit tests for the new features.

This project uses DCO. Be sure to sign off your commits using the -s flag or adding Signed-off-By: Name<Email> in the commit message.

Example

git commit -s -m 'Add new feature'

Citing ART

If you use ART for research, please consider citing the following reference paper:

@article{art2018,
    title = {Adversarial Robustness Toolbox v0.7.0},
    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}
}

Release files for adversarial-robustness-toolbox 0.7.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for adversarial-robustness-toolbox 0.7.0
File	Size	Uploaded
Adversarial Robustness Toolbox-0.7.0.tar.gz	135.1 kB	Apr 1, 2019	Details

Built distribution (wheel)

Table of built distributions (wheels) for adversarial-robustness-toolbox 0.7.0
File	Interpreter	ABI	Platform	Reset
Adversarial_Robustness_Toolbox-0.7.0-py3-none-any.whl 214.3 kB Apr 1, 2019	Python 3	none	any	Details

Total release size: 349.3 kB

Release files / Adversarial Robustness Toolbox-0.7.0.tar.gz

Download URL	Adversarial Robustness Toolbox-0.7.0.tar.gz
Size	135.1 kB
Tags	Source
SHA-256 checksum How to use checksums	`1999a49c873e42718bfc5aa27bd9605be11aacf1b9180c093873551d77472d22`
BLAKE2b-256 checksum How to use checksums	`6077714cedd86ae584b2c65585d346de4b3b04e3c6e0149815f08a71a0aa70e8`
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Uploaded via	`twine/1.13.0 pkginfo/1.5.0.1 requests/2.21.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.31.1 CPython/3.6.0`

Release files / Adversarial_Robustness_Toolbox-0.7.0-py3-none-any.whl

Download URL	Adversarial_Robustness_Toolbox-0.7.0-py3-none-any.whl
Size	214.3 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`c1023195085571af7de584d2c0dfd6400cdde7cfeaaf73e939f23cd27fcc4262`
BLAKE2b-256 checksum How to use checksums	`ca989b7de5636dc6681181f83b618ebcf5cce4563a5809a13a15e0dd10746965`
Upload date	Apr 1, 2019
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/1.13.0 pkginfo/1.5.0.1 requests/2.21.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.31.1 CPython/3.6.0`