Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Query-Free Attacks on Industry-Grade Face Recognition Systems under
  Resource Constraints   To launch black-box attacks against a Deep Neural Network (DNN) based Face
Recognition (FR) system, one needs to build \textit{substitute} models to
simulate the target model, so the adversarial examples discovered from
substitute models could also mislead the target model. Such
\textit{transferability} is achieved in recent studies through querying the
target model to obtain data for training the substitute models. A real-world
target, likes the FR system of law enforcement, however, is less accessible to
the adversary. To attack such a system, a substitute model with similar quality
as the target model is needed to identify their common defects. This is hard
since the adversary often does not have the enough resources to train such a
powerful model (hundreds of millions of images and rooms of GPUs are needed to
train a commercial FR system).
  We found in our research, however, that a resource-constrained adversary
could still effectively approximate the target model's capability to recognize
\textit{specific} individuals, by training \textit{biased} substitute models on
additional images of those victims whose identities the attacker want to cover
or impersonate. This is made possible by a new property we discovered, called
\textit{Nearly Local Linearity} (NLL), which models the observation that an
ideal DNN model produces the image representations (embeddings) whose distances
among themselves truthfully describe the human perception of the differences
among the input images. By simulating this property around the victim's images,
we significantly improve the transferability of black-box impersonation attacks
by nearly 50\%. Particularly, we successfully attacked a commercial system
trained over 20 million images, using 4 million images and 1/5 of the training
time but achieving 62\% transferability in an impersonation attack and 89\% in
a dodging attack.

18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
16
The categories of this paper are: stat
17
The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat
14
The categories of this paper are: math-ph
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
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The categories of this paper are: funct-an
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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: mtrl-th
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The categories of this paper are: atom-ph
3
The categories of this paper are: physics