Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MAZE: Data-Free Model Stealing Attack Using Zeroth-Order Gradient
  Estimation   Model Stealing (MS) attacks allow an adversary with black-box access to a
Machine Learning model to replicate its functionality, compromising the
confidentiality of the model. Such attacks train a clone model by using the
predictions of the target model for different inputs. The effectiveness of such
attacks relies heavily on the availability of data necessary to query the
target model. Existing attacks either assume partial access to the dataset of
the target model or availability of an alternate dataset with semantic
similarities.
  This paper proposes MAZE -- a data-free model stealing attack using
zeroth-order gradient estimation. In contrast to prior works, MAZE does not
require any data and instead creates synthetic data using a generative model.
Inspired by recent works in data-free Knowledge Distillation (KD), we train the
generative model using a disagreement objective to produce inputs that maximize
disagreement between the clone and the target model. However, unlike the
white-box setting of KD, where the gradient information is available, training
a generator for model stealing requires performing black-box optimization, as
it involves accessing the target model under attack. MAZE relies on
zeroth-order gradient estimation to perform this optimization and enables a
highly accurate MS attack.
  Our evaluation with four datasets shows that MAZE provides a normalized clone
accuracy in the range of 0.91x to 0.99x, and outperforms even the recent
attacks that rely on partial data (JBDA, clone accuracy 0.13x to 0.69x) and
surrogate data (KnockoffNets, clone accuracy 0.52x to 0.97x). We also study an
extension of MAZE in the partial-data setting and develop MAZE-PD, which
generates synthetic data closer to the target distribution. MAZE-PD further
improves the clone accuracy (0.97x to 1.0x) and reduces the query required for
the attack by 2x-24x.

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The categories of this paper are: gr-qc
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The categories of this paper are: q-bio
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The categories of this paper are: atom-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: chem-ph
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The categories of this paper are: solv-int
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The categories of this paper are: patt-sol
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The categories of this paper are: dg-ga
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The categories of this paper are: cond-mat
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The categories of this paper are: math
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The categories of this paper are: astro-ph
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The categories of this paper are: eess
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The categories of this paper are: funct-an
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The categories of this paper are: quant-ph
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The categories of this paper are: supr-con