  Recent research has shown that Machine Learning/Deep Learning (ML/DL) models
are particularly vulnerable to adversarial perturbations, which are small
changes made to the input data in order to fool a machine learning classifier.
The Digital Twin, which is typically described as consisting of a physical
entity, a virtual counterpart, and the data connections in between, is
increasingly being investigated as a means of improving the performance of
physical entities by leveraging computational techniques, which are enabled by
the virtual counterpart. This paper explores the susceptibility of Digital Twin
(DT), a virtual model designed to accurately reflect a physical object using
ML/DL classifiers that operate as Cyber Physical Systems (CPS), to adversarial
attacks. As a proof of concept, we first formulate a DT of a vehicular system
using a deep neural network architecture and then utilize it to launch an
adversarial attack. We attack the DT model by perturbing the input to the
trained model and show how easily the model can be broken with white-box
attacks.
