Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: First-order Adversarial Vulnerability of Neural Networks and Input
  Dimension   Over the past few years, neural networks were proven vulnerable to
adversarial images: targeted but imperceptible image perturbations lead to
drastically different predictions. We show that adversarial vulnerability
increases with the gradients of the training objective when viewed as a
function of the inputs. Surprisingly, vulnerability does not depend on network
topology: for many standard network architectures, we prove that at
initialization, the $\ell_1$-norm of these gradients grows as the square root
of the input dimension, leaving the networks increasingly vulnerable with
growing image size. We empirically show that this dimension dependence persists
after either usual or robust training, but gets attenuated with higher
regularization.

15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
13
The categories of this paper are: quant-ph
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess
31
The categories of this paper are: ao-sci