Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Attacking the Madry Defense Model with $L_1$-based Adversarial Examples   The Madry Lab recently hosted a competition designed to test the robustness
of their adversarially trained MNIST model. Attacks were constrained to perturb
each pixel of the input image by a scaled maximal $L_\infty$ distortion
$\epsilon$ = 0.3. This discourages the use of attacks which are not optimized
on the $L_\infty$ distortion metric. Our experimental results demonstrate that
by relaxing the $L_\infty$ constraint of the competition, the elastic-net
attack to deep neural networks (EAD) can generate transferable adversarial
examples which, despite their high average $L_\infty$ distortion, have minimal
visual distortion. These results call into question the use of $L_\infty$ as a
sole measure for visual distortion, and further demonstrate the power of EAD at
generating robust adversarial examples.

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The categories of this paper are: q-fin
25
The categories of this paper are: patt-sol
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The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
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The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th
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The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
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The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
4
The categories of this paper are: cond-mat
24
The categories of this paper are: dg-ga
7
The categories of this paper are: hep-th