Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Intermediate Level Adversarial Attack for Enhanced Transferability   Neural networks are vulnerable to adversarial examples, malicious inputs
crafted to fool trained models. Adversarial examples often exhibit black-box
transfer, meaning that adversarial examples for one model can fool another
model. However, adversarial examples may be overfit to exploit the particular
architecture and feature representation of a source model, resulting in
sub-optimal black-box transfer attacks to other target models. This leads us to
introduce the Intermediate Level Attack (ILA), which attempts to fine-tune an
existing adversarial example for greater black-box transferability by
increasing its perturbation on a pre-specified layer of the source model. We
show that our method can effectively achieve this goal and that we can decide a
nearly-optimal layer of the source model to perturb without any knowledge of
the target models.

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The categories of this paper are: patt-sol
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The categories of this paper are: solv-int
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The categories of this paper are: bayes-an
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The categories of this paper are: q-alg
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The categories of this paper are: funct-an
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The categories of this paper are: econ
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The categories of this paper are: q-fin
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The categories of this paper are: eess
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The categories of this paper are: hep-ex
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The categories of this paper are: nucl-ex
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The categories of this paper are: supr-con
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The categories of this paper are: q-bio
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The categories of this paper are: math
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The categories of this paper are: alg-geom
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The categories of this paper are: math-ph
3
The categories of this paper are: physics