  Spiking neural networks (SNNs) provide an energy-efficient alternative to a
variety of artificial neural network (ANN) based AI applications. As the
progress in neuromorphic computing with SNNs expands their use in applications,
the problem of adversarial robustness of SNNs becomes more pronounced. To the
contrary of the widely explored end-to-end adversarial training based
solutions, we address the limited progress in scalable robust SNN training
methods by proposing an adversarially robust ANN-to-SNN conversion algorithm.
Our method provides an efficient approach to embrace various computationally
demanding robust learning objectives that have been proposed for ANNs. During a
post-conversion robust finetuning phase, our method adversarially optimizes
both layer-wise firing thresholds and synaptic connectivity weights of the SNN
to maintain transferred robustness gains from the pre-trained ANN. We perform
experimental evaluations in a novel setting proposed to rigorously assess the
robustness of SNNs, where numerous adaptive adversarial attacks that account
for the spike-based operation dynamics are considered. Results show that our
approach yields a scalable state-of-the-art solution for adversarially robust
deep SNNs with low-latency.
