Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards Achieving Adversarial Robustness by Enforcing Feature
  Consistency Across Bit Planes   As humans, we inherently perceive images based on their predominant features,
and ignore noise embedded within lower bit planes. On the contrary, Deep Neural
Networks are known to confidently misclassify images corrupted with
meticulously crafted perturbations that are nearly imperceptible to the human
eye. In this work, we attempt to address this problem by training networks to
form coarse impressions based on the information in higher bit planes, and use
the lower bit planes only to refine their prediction. We demonstrate that, by
imposing consistency on the representations learned across differently
quantized images, the adversarial robustness of networks improves significantly
when compared to a normally trained model. Present state-of-the-art defenses
against adversarial attacks require the networks to be explicitly trained using
adversarial samples that are computationally expensive to generate. While such
methods that use adversarial training continue to achieve the best results,
this work paves the way towards achieving robustness without having to
explicitly train on adversarial samples. The proposed approach is therefore
faster, and also closer to the natural learning process in humans.

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The categories of this paper are: cond-mat
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The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
8
The categories of this paper are: hep-ex
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The categories of this paper are: astro-ph
34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn
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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: adap-org
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The categories of this paper are: eess
32
The categories of this paper are: atom-ph
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The categories of this paper are: q-fin
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The categories of this paper are: bayes-an
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The categories of this paper are: gr-qc
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The categories of this paper are: comp-gas