  Deep Learning (DL) algorithms have gained popularity owing to their practical
problem-solving capacity. However, they suffer from a serious integrity threat,
i.e., their vulnerability to adversarial attacks. In the quest for DL
trustworthiness, recent works claimed the inherent robustness of Spiking Neural
Networks (SNNs) to these attacks, without considering the variability in their
structural spiking parameters. This paper explores the security enhancement of
SNNs through internal structural parameters. Specifically, we investigate the
SNNs robustness to adversarial attacks with different values of the neuron's
firing voltage thresholds and time window boundaries. We thoroughly study SNNs
security under different adversarial attacks in the strong white-box setting,
with different noise budgets and under variable spiking parameters. Our results
show a significant impact of the structural parameters on the SNNs' security,
and promising sweet spots can be reached to design trustworthy SNNs with 85%
higher robustness than a traditional non-spiking DL system. To the best of our
knowledge, this is the first work that investigates the impact of structural
parameters on SNNs robustness to adversarial attacks. The proposed
contributions and the experimental framework is available online to the
community for reproducible research.
