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foolbox

Python

Version 3.3.4

Download

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Documentation: The API documentation can be found on ReadTheDocs.

Quickstart

pip install foolbox

Foolbox is tested with Python 3.8 and newer - however, it will most likely also work with version 3.6 - 3.8. To use it with PyTorch, TensorFlow, or JAX, the respective framework needs to be installed separately. These frameworks are not declared as dependencies because not everyone wants to use and thus install all of them and because some of these packages have different builds for different architectures and CUDA versions. Besides that, all essential dependencies are automatically installed.

You can see the versions we currently use for testing in the Compatibility section below, but newer versions are in general expected to work.

? Example

import foolbox as fb

model = ...
fmodel = fb.PyTorchModel(model, bounds=(0, 1))

attack = fb.attacks.LinfPGD()
epsilons = [0.0, 0.001, 0.01, 0.03, 0.1, 0.3, 0.5, 1.0]
_, advs, success = attack(fmodel, images, labels, epsilons=epsilons)

More examples can be found in the examples folder, e.g. a full ResNet-18 example.

? Citation

If you use Foolbox for your work, please cite our JOSS paper on Foolbox Native (i.e., Foolbox 3.0) and our ICML workshop paper on Foolbox using the following BibTeX entries:

@article{rauber2017foolboxnative,
  doi = {10.21105/joss.02607},
  url = {https://doi.org/10.21105/joss.02607},
  year = {2020},
  publisher = {The Open Journal},
  volume = {5},
  number = {53},
  pages = {2607},
  author = {Jonas Rauber and Roland Zimmermann and Matthias Bethge and Wieland Brendel},
  title = {Foolbox Native: Fast adversarial attacks to benchmark the robustness of machine learning models in PyTorch, TensorFlow, and JAX},
  journal = {Journal of Open Source Software}
}

@inproceedings{rauber2017foolbox,
  title={Foolbox: A Python toolbox to benchmark the robustness of machine learning models},
  author={Rauber, Jonas and Brendel, Wieland and Bethge, Matthias},
  booktitle={Reliable Machine Learning in the Wild Workshop, 34th International Conference on Machine Learning},
  year={2017},
  url={http://arxiv.org/abs/1707.04131},
}

? Contributions

We welcome contributions of all kind, please have a look at our development guidelines. In particular, you are invited to contribute new adversarial attacks. If you would like to help, you can also have a look at the issues that are marked with contributions welcome.

Questions?

If you have a question or need help, feel free to open an issue on GitHub. Once GitHub Discussions becomes publicly available, we will switch to that.