With the mainstream integration of machine learning into security-sensitive domains such as
healthcare and finance, concerns about data privacy have intensified. Conventional artificial …

PGD-based and FGSM-based are two popular adversarial training (AT) approaches for
obtaining adversarially robust models. Compared with PGD-based AT, FGSM-based one is …

Event-based sensing using dynamic vision sensors is gaining traction in low-power vision
applications. Spiking neural networks work well with the sparse nature of event-based data …

This paper investigates the system security problem of cyber-physical systems (CPSs),
which is not only more practical but also more significant to deal with than the detecting …

Spiking neural networks (SNNs), inspired by the neural circuits of the brain, are promising in
achieving high computational efficiency with biological fidelity. Nevertheless, it is quite …

Existing works indicate that Spiking Neural Networks (SNNs) are resilient to adversarial
attacks by testing against few attack models. This paper studies adversarial attacks on SNNs …

Spiking Neural Networks are a class of artificial neural networks that process information as
discrete spikes. The time and energy consumed in SNN implementations is strongly …

Abstract Spiking Neural Networks (SNNs) are increasingly deployed in applications on
resource constraint embedding systems due to their low power. Unfortunately, SNNs are …

With the future 6G era, spiking neural networks (SNNs) can be powerful processing tools in
various areas due to their strong artificial intelligence (AI) processing capabilities, such as …

On the road toward artificial general intelligence (AGI), two solution paths have been
explored: neuroscience-driven neuromorphic computing such as spiking neural networks …