Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Enhancing Adversarial Robustness via Test-time Transformation Ensembling   Deep learning models are prone to being fooled by imperceptible perturbations
known as adversarial attacks. In this work, we study how equipping models with
Test-time Transformation Ensembling (TTE) can work as a reliable defense
against such attacks. While transforming the input data, both at train and test
times, is known to enhance model performance, its effects on adversarial
robustness have not been studied. Here, we present a comprehensive empirical
study of the impact of TTE, in the form of widely-used image transforms, on
adversarial robustness. We show that TTE consistently improves model robustness
against a variety of powerful attacks without any need for re-training, and
that this improvement comes at virtually no trade-off with accuracy on clean
samples. Finally, we show that the benefits of TTE transfer even to the
certified robustness domain, in which TTE provides sizable and consistent
improvements.

20
The categories of this paper are: acc-phys
19
The categories of this paper are: eess
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
16
The categories of this paper are: stat
31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th
2
The categories of this paper are: math
23
The categories of this paper are: solv-int