Software repository
Paper review
Download paper
Software archive
Review
Editor: @terrytangyuan (all papers)
Reviewers: @GregaVrbancic (all reviews), @ethanwharris (all reviews)
Authors
Jonas Rauber (0000-0001-6795-9441), Roland Zimmermann, Matthias Bethge, Wieland Brendel
Citation
Rauber et al., (2020). Foolbox Native: Fast adversarial attacks to benchmark the robustness of machine learning models in PyTorch, TensorFlow, and JAX. Journal of Open Source Software, 5(53), 2607, https://doi.org/10.21105/joss.02607
@article{Rauber2020,
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}
}
Tags
python machine learning adversarial attacks neural networks pytorch tensorflow jax keras eagerpy
Altmetrics
Markdown badge
License
Authors of JOSS papers retain copyright.
This work is licensed under a Creative Commons Attribution 4.0 International License.
