Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Automating Defense Against Adversarial Attacks: Discovery of
  Vulnerabilities and Application of Multi-INT Imagery to Protect Deployed
  Models   Image classification is a common step in image recognition for machine
learning in overhead applications. When applying popular model architectures
like MobileNetV2, known vulnerabilities expose the model to counter-attacks,
either mislabeling a known class or altering box location. This work proposes
an automated approach to defend these models. We evaluate the use of
multi-spectral image arrays and ensemble learners to combat adversarial
attacks. The original contribution demonstrates the attack, proposes a remedy,
and automates some key outcomes for protecting the model's predictions against
adversaries. In rough analogy to defending cyber-networks, we combine
techniques from both offensive ("red team") and defensive ("blue team")
approaches, thus generating a hybrid protective outcome ("green team"). For
machine learning, we demonstrate these methods with 3-color channels plus
infrared for vehicles. The outcome uncovers vulnerabilities and corrects them
with supplemental data inputs commonly found in overhead cases particularly.

20
The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
16
The categories of this paper are: stat
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
36
The categories of this paper are: cmp-lg
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin