Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards Query Efficient Black-box Attacks: An Input-free Perspective   Recent studies have highlighted that deep neural networks (DNNs) are
vulnerable to adversarial attacks, even in a black-box scenario. However, most
of the existing black-box attack algorithms need to make a huge amount of
queries to perform attacks, which is not practical in the real world. We note
one of the main reasons for the massive queries is that the adversarial example
is required to be visually similar to the original image, but in many cases,
how adversarial examples look like does not matter much. It inspires us to
introduce a new attack called \emph{input-free} attack, under which an
adversary can choose an arbitrary image to start with and is allowed to add
perceptible perturbations on it. Following this approach, we propose two
techniques to significantly reduce the query complexity. First, we initialize
an adversarial example with a gray color image on which every pixel has roughly
the same importance for the target model. Then we shrink the dimension of the
attack space by perturbing a small region and tiling it to cover the input
image. To make our algorithm more effective, we stabilize a projected gradient
ascent algorithm with momentum, and also propose a heuristic approach for
region size selection. Through extensive experiments, we show that with only
1,701 queries on average, we can perturb a gray image to any target class of
ImageNet with a 100\% success rate on InceptionV3. Besides, our algorithm has
successfully defeated two real-world systems, the Clarifai food detection API
and the Baidu Animal Identification API.

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The categories of this paper are: alg-geom
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The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
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The categories of this paper are: q-alg
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The categories of this paper are: adap-org
2
The categories of this paper are: math
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The categories of this paper are: q-bio
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The categories of this paper are: mtrl-th
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The categories of this paper are: math-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: eess
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The categories of this paper are: bayes-an
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The categories of this paper are: acc-phys
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The categories of this paper are: chem-ph