Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Measuring the Contribution of Multiple Model Representations in
  Detecting Adversarial Instances   Deep learning models have been used for a wide variety of tasks. They are
prevalent in computer vision, natural language processing, speech recognition,
and other areas. While these models have worked well under many scenarios, it
has been shown that they are vulnerable to adversarial attacks. This has led to
a proliferation of research into ways that such attacks could be identified
and/or defended against. Our goal is to explore the contribution that can be
attributed to using multiple underlying models for the purpose of adversarial
instance detection. Our paper describes two approaches that incorporate
representations from multiple models for detecting adversarial examples. We
devise controlled experiments for measuring the detection impact of
incrementally utilizing additional models. For many of the scenarios we
consider, the results show that performance increases with the number of
underlying models used for extracting representations.

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The categories of this paper are: math
12
The categories of this paper are: nucl-th
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
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The categories of this paper are: nucl-ex
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The categories of this paper are: quant-ph
29
The categories of this paper are: funct-an
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The categories of this paper are: q-fin
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-lat
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The categories of this paper are: eess
10
The categories of this paper are: q-bio
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The categories of this paper are: hep-th
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The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
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The categories of this paper are: physics