Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Detecting Potential Local Adversarial Examples for Human-Interpretable
  Defense   Machine learning models are increasingly used in the industry to make
decisions such as credit insurance approval. Some people may be tempted to
manipulate specific variables, such as the age or the salary, in order to get
better chances of approval. In this ongoing work, we propose to discuss, with a
first proposition, the issue of detecting a potential local adversarial example
on classical tabular data by providing to a human expert the locally critical
features for the classifier's decision, in order to control the provided
information and avoid a fraud.

3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph
2
The categories of this paper are: math
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
25
The categories of this paper are: patt-sol
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci