Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Isolated and Ensemble Audio Preprocessing Methods for Detecting
  Adversarial Examples against Automatic Speech Recognition   An adversarial attack is an exploitative process in which minute alterations
are made to natural inputs, causing the inputs to be misclassified by neural
models. In the field of speech recognition, this has become an issue of
increasing significance. Although adversarial attacks were originally
introduced in computer vision, they have since infiltrated the realm of speech
recognition. In 2017, a genetic attack was shown to be quite potent against the
Speech Commands Model. Limited-vocabulary speech classifiers, such as the
Speech Commands Model, are used in a variety of applications, particularly in
telephony; as such, adversarial examples produced by this attack pose as a
major security threat. This paper explores various methods of detecting these
adversarial examples with combinations of audio preprocessing. One particular
combined defense incorporating compressions, speech coding, filtering, and
audio panning was shown to be quite effective against the attack on the Speech
Commands Model, detecting audio adversarial examples with 93.5% precision and
91.2% recall.

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The categories of this paper are: q-bio
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The categories of this paper are: bayes-an
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The categories of this paper are: funct-an
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The categories of this paper are: ao-sci
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The categories of this paper are: q-fin
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The categories of this paper are: nlin
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The categories of this paper are: hep-lat
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The categories of this paper are: adap-org
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The categories of this paper are: cmp-lg
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The categories of this paper are: cond-mat
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The categories of this paper are: gr-qc
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The categories of this paper are: stat
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The categories of this paper are: astro-ph
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The categories of this paper are: solv-int
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The categories of this paper are: mtrl-th
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The categories of this paper are: math