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Foolbox Native: Fast adversarial attacks to benchmark the robustness of machine learning models in PyTorch, TensorFlow, and JAX

Jonas Rauber, R. Zimmermann, Matthias Bethge, Wieland Brendel

The Journal of Open Source Software · 2020 · 161 citations · 7 references

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Abstract

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

References

7

PyTorch: An Imperative Style, High-Performance Deep Learning Library

Adam Paszke, Sam Gross, Francisco Massa et al. · arXiv (Cornell University) · 2019 · 16.2K citations · Full text

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TensorFlow: a system for large-scale machine learning

Martı́n Abadi, Paul Barham, Jianmin Chen et al. · Operating Systems Design and Implementation · 2016 · 6.4K citations

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Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box\n Machine Learning Models

Wieland Brendel, Jonas Rauber, Matthias Bethge · arXiv (Cornell University) · 2017 · 893 citations · Full text

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HopSkipJumpAttack: A Query-Efficient Decision-Based Attack

Jianbo Chen, Michael I. Jordan, Martin J. Wainwright · 2020 · 610 citations · Full text

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Foolbox v0.8.0: A Python toolbox to benchmark the robustness of machine learning models

Jonas Rauber, Wieland Brendel, Matthias Bethge · arXiv (Cornell University) · 2017 · 352 citations · Full text

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