Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Minimizing Perceived Image Quality Loss Through Adversarial Attack
  Scoping   Neural networks are now actively being used for computer vision tasks in
security critical areas such as robotics, face recognition, autonomous vehicles
yet their safety is under question after the discovery of adversarial attacks.
In this paper we develop simplified adversarial attack algorithms based on a
scoping idea, which enables execution of fast adversarial attacks that minimize
structural image quality (SSIM) loss, allows performing efficient transfer
attacks with low target inference network call count and opens a possibility of
an attack using pen-only drawings on a paper for the MNIST handwritten digit
dataset. The presented adversarial attack analysis and the idea of attack
scoping can be easily expanded to different datasets, thus making the paper's
results applicable to a wide range of practical tasks.

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The categories of this paper are: chao-dyn
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The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph
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The categories of this paper are: comp-gas
28
The categories of this paper are: alg-geom
18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph
21
The categories of this paper are: adap-org
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The categories of this paper are: math-ph
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The categories of this paper are: solv-int
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The categories of this paper are: ao-sci
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The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
2
The categories of this paper are: math