Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adversarial Attacks on Machinery Fault Diagnosis   Despite the great progress of neural network-based (NN-based) machinery fault
diagnosis methods, their robustness has been largely neglected, for they can be
easily fooled through adding imperceptible perturbation to the input. For fault
diagnosis problems, in this paper, we reformulate various adversarial attacks
and intensively investigate them under untargeted and targeted conditions.
Experimental results on six typical NN-based models show that accuracies of the
models are greatly reduced by adding small perturbations. We further propose a
simple, efficient and universal scheme to protect the victim models. This work
provides an in-depth look at adversarial examples of machinery vibration
signals for developing protection methods against adversarial attack and
improving the robustness of NN-based models.

25
The categories of this paper are: patt-sol
7
The categories of this paper are: hep-th
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
17
The categories of this paper are: q-fin
5
The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn