RobustBench
A standardized benchmark for adversarial robustness
The goal of RobustBench is to systematically track the real progress in adversarial robustness. There are already more than 2'000 papers on this topic, but it is still unclear which approaches really work and which only lead to overestimated robustness. We start from benchmarking common corruptions, \(\ell_\infty\)- and \(\ell_2\)-robustness since these are the most studied settings in the literature. We use AutoAttack, an ensemble of white-box and black-box attacks, to standardize the evaluation (for details see our paper) of the \(\ell_p\) robustness and CIFAR-10-C for the evaluation of robustness to common corruptions. Additionally, we open source the RobustBench library that contains models used for the leaderboard to facilitate their usage for downstream applications.
Up-to-date leaderboard based
on 90+ models
Unified access to 60+ state-of-the-art
robust models via
Model Zoo
Model Zoo
# !pip install git+https://github.com/RobustBench/robustbench@v0.1 from robustbench.utils import load_model # Load a model from the model zoo model = load_model(model_name='Carmon2019Unlabeled', dataset='cifar10', threat_model='Linf') # Evaluate the Linf robustness of the model using AutoAttack from robustbench.eval import benchmark clean_acc, robust_acc = benchmark(model, dataset='cifar10', threat_model='Linf')
Leaderboard: CIFAR-10, \( \ell_\infty = 8/255 \), Untargeted, AutoAttack
FAQ
➤ Wait, how does this leaderboard differ from the
AutoAttack leaderboard? 🤔
The AutoAttack leaderboard is
maintained simultaneously
with the RobustBench L2 / Linf leaderboards by Francesco
Croce, and all
the changes to either of them will be synchronized (given that the 3 restrictions on the models are met
for the RobustBench leaderboard). One can see the current L2 / Linf
RobustBench leaderboard as a
continuously updated fork of the AutoAttack leaderboard
extended
by adaptive evaluations, Model Zoo, and clear restrictions on the models we accept. And in the future,
we will extend RobustBench with other threat models and potentially with a different
standardized attack
if it's shown to perform better than AutoAttack.
➤ Wait, how is it different from
robust-ml.org? 🤔
robust-ml.org focuses on
adaptive evaluations, but we provide a
standardized benchmark. Adaptive evaluations are
great (e.g., see
Tramer et al., 2020),
but very time-consuming and cannot be standardized. Instead, we argue
that one can estimate robustness accurately without adaptive
attacks but for this one has to introduce some restrictions on the
considered models. See our paper for more details.
➤ How is it related to libraries like
foolbox /
cleverhans /
advertorch? 🤔
These libraries provide implementations of different
attacks. Besides the standardized benchmark,
RobustBench additionally provides a repository of the
most robust models. So you can start using the robust models in one
line of code (see the tutorial
here).
➤ Why is Lp-robustness still interesting in 2021? 🤔
There are numerous interesting applications of Lp-robustness that
span transfer learning (Salman et al. (2020),
Utrera et al. (2020)),
interpretability (Tsipras et al. (2018), Kaur et al. (2019),
Engstrom et al. (2019)), security (Tramèr et al. (2018),
Saadatpanah et al. (2019)), generalization (Xie et al. (2019), Zhu et al. (2019),
Bochkovskiy et al. (2020)), robustness to unseen perturbations
(Xie et al. (2019), Kang et al. (2019)),
stabilization of GAN training (Zhong et al. (2020)).
➤ What about verified adversarial robustness? 🤔
We specifically focus on defenses which improve
empirical robustness, given the lack of clarity regarding
which approaches really improve robustness and which only make some
particular attacks unsuccessful. For methods targeting verified
robustness, we encourage the readers to check out
Salman et al. (2019)
and Li et al. (2020).
➤ What if I have a better attack than the one used in this
benchmark? 🤔
We will be happy to add a better attack or any adaptive evaluation
that would complement our default standardized attacks.
Citation
@article{croce2020robustbench, title={RobustBench: a standardized adversarial robustness benchmark}, author={Croce, Francesco and Andriushchenko, Maksym and Sehwag, Vikash and Debenedetti, Edoardo and Flammarion, Nicolas and Chiang, Mung and Mittal, Prateek and Matthias Hein}, journal={arXiv preprint arXiv:2010.09670}, year={2020} }
Contribute to RobustBench!
