  In neural network (NN) security, safeguarding model integrity and resilience
against adversarial attacks has become paramount. This study investigates the
application of stochastic computing (SC) as a novel mechanism to fortify NN
models. The primary objective is to assess the efficacy of SC to mitigate the
deleterious impact of attacks on NN results. Through a series of rigorous
experiments and evaluations, we explore the resilience of NNs employing SC when
subjected to adversarial attacks. Our findings reveal that SC introduces a
robust layer of defense, significantly reducing the susceptibility of networks
to attack-induced alterations in their outcomes. This research contributes
novel insights into the development of more secure and reliable NN systems,
essential for applications in sensitive domains where data integrity is of
utmost concern.
