Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Can Adversarially Robust Learning Leverage Computational Hardness?   Making learners robust to adversarial perturbation at test time (i.e.,
evasion attacks) or training time (i.e., poisoning attacks) has emerged as a
challenging task. It is known that for some natural settings, sublinear
perturbations in the training phase or the testing phase can drastically
decrease the quality of the predictions. These negative results, however, are
information theoretic and only prove the existence of such successful
adversarial perturbations. A natural question for these settings is whether or
not we can make classifiers computationally robust to polynomial-time attacks.
  In this work, we prove strong barriers against achieving such envisioned
computational robustness both for evasion and poisoning attacks. In particular,
we show that if the test instances come from a product distribution (e.g.,
uniform over $\{0,1\}^n$ or $[0,1]^n$, or isotropic $n$-variate Gaussian) and
that there is an initial constant error, then there exists a polynomial-time
attack that finds adversarial examples of Hamming distance $O(\sqrt n)$. For
poisoning attacks, we prove that for any learning algorithm with sample
complexity $m$ and any efficiently computable "predicate" defining some "bad"
property $B$ for the produced hypothesis (e.g., failing on a particular test)
that happens with an initial constant probability, there exist polynomial-time
online poisoning attacks that tamper with $O (\sqrt m)$ many examples, replace
them with other correctly labeled examples, and increases the probability of
the bad event $B$ to $\approx 1$.
  Both of our poisoning and evasion attacks are black-box in how they access
their corresponding components of the system (i.e., the hypothesis, the
concept, and the learning algorithm) and make no further assumptions about the
classifier or the learning algorithm producing the classifier.

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The categories of this paper are: nucl-th
15
The categories of this paper are: nucl-ex
22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
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The categories of this paper are: alg-geom
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The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
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The categories of this paper are: nlin
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The categories of this paper are: mtrl-th
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The categories of this paper are: gr-qc
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-lat
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The categories of this paper are: adap-org
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The categories of this paper are: acc-phys
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The categories of this paper are: comp-gas