Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving Robustness with Image Filtering   Adversarial robustness is one of the most challenging problems in Deep
Learning and Computer Vision research. All the state-of-the-art techniques
require a time-consuming procedure that creates cleverly perturbed images. Due
to its cost, many solutions have been proposed to avoid Adversarial Training.
However, all these attempts proved ineffective as the attacker manages to
exploit spurious correlations among pixels to trigger brittle features
implicitly learned by the model. This paper first introduces a new image
filtering scheme called Image-Graph Extractor (IGE) that extracts the
fundamental nodes of an image and their connections through a graph structure.
By leveraging the IGE representation, we build a new defense method, Filtering
As a Defense, that does not allow the attacker to entangle pixels to create
malicious patterns. Moreover, we show that data augmentation with filtered
images effectively improves the model's robustness to data corruption. We
validate our techniques on CIFAR-10, CIFAR-100, and ImageNet.

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The categories of this paper are: plasm-ph
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The categories of this paper are: nlin
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The categories of this paper are: bayes-an
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The categories of this paper are: atom-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: chao-dyn
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The categories of this paper are: acc-phys
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The categories of this paper are: ao-sci
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The categories of this paper are: solv-int
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The categories of this paper are: adap-org
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The categories of this paper are: q-bio
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The categories of this paper are: math-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: astro-ph
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The categories of this paper are: dg-ga