Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: How to compare adversarial robustness of classifiers from a global
  perspective   Adversarial robustness of machine learning models has attracted considerable
attention over recent years. Adversarial attacks undermine the reliability of
and trust in machine learning models, but the construction of more robust
models hinges on a rigorous understanding of adversarial robustness as a
property of a given model. Point-wise measures for specific threat models are
currently the most popular tool for comparing the robustness of classifiers and
are used in most recent publications on adversarial robustness. In this work,
we use recently proposed robustness curves to show that point-wise measures
fail to capture important global properties that are essential to reliably
compare the robustness of different classifiers. We introduce new ways in which
robustness curves can be used to systematically uncover these properties and
provide concrete recommendations for researchers and practitioners when
assessing and comparing the robustness of trained models. Furthermore, we
characterize scale as a way to distinguish small and large perturbations, and
relate it to inherent properties of data sets, demonstrating that robustness
thresholds must be chosen accordingly. We release code to reproduce all
experiments presented in this paper, which includes a Python module to
calculate robustness curves for arbitrary data sets and classifiers, supporting
a number of frameworks, including TensorFlow, PyTorch and JAX.

23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
21
The categories of this paper are: adap-org