Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Attack Type Agnostic Perceptual Enhancement of Adversarial Images   Adversarial images are samples that are intentionally modified to deceive
machine learning systems. They are widely used in applications such as CAPTHAs
to help distinguish legitimate human users from bots. However, the noise
introduced during the adversarial image generation process degrades the
perceptual quality and introduces artificial colours; making it also difficult
for humans to classify images and recognise objects. In this letter, we propose
a method to enhance the perceptual quality of these adversarial images. The
proposed method is attack type agnostic and could be used in association with
the existing attacks in the literature. Our experiments show that the generated
adversarial images have lower Euclidean distance values while maintaining the
same adversarial attack performance. Distances are reduced by 5.88% to 41.27%
with an average reduction of 22% over the different attack and network types.

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The categories of this paper are: quant-ph
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The categories of this paper are: math
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The categories of this paper are: atom-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: q-alg
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The categories of this paper are: gr-qc
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The categories of this paper are: mtrl-th
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The categories of this paper are: chem-ph
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The categories of this paper are: math-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: acc-phys
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The categories of this paper are: adap-org
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The categories of this paper are: plasm-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: q-fin
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The categories of this paper are: econ