Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adversarial defense for automatic speaker verification by cascaded
  self-supervised learning models   Automatic speaker verification (ASV) is one of the core technologies in
biometric identification. With the ubiquitous usage of ASV systems in
safety-critical applications, more and more malicious attackers attempt to
launch adversarial attacks at ASV systems. In the midst of the arms race
between attack and defense in ASV, how to effectively improve the robustness of
ASV against adversarial attacks remains an open question. We note that the
self-supervised learning models possess the ability to mitigate superficial
perturbations in the input after pretraining. Hence, with the goal of effective
defense in ASV against adversarial attacks, we propose a standard and
attack-agnostic method based on cascaded self-supervised learning models to
purify the adversarial perturbations. Experimental results demonstrate that the
proposed method achieves effective defense performance and can successfully
counter adversarial attacks in scenarios where attackers may either be aware or
unaware of the self-supervised learning models.

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The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
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The categories of this paper are: q-fin
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The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
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The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
4
The categories of this paper are: cond-mat
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The categories of this paper are: alg-geom
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The categories of this paper are: atom-ph
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The categories of this paper are: plasm-ph
24
The categories of this paper are: dg-ga
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The categories of this paper are: ao-sci
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The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an