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 numerous adaptive adversarial settings that account for the spike-based operation dynamics of SNNs, and show that our approach yields a scalable state-of-the-art solution for adversarially robust deep SNNs with low-latency.

翻译：脉冲神经网络（SNN）为众多基于人工神经网络（ANN）的人工智能应用提供了一种高能效替代方案。随着基于SNN的神经形态计算在应用中的拓展，SNN的对抗鲁棒性问题日益凸显。与广泛研究的端到端对抗训练方法相反，我们提出了一种具有对抗鲁棒性的ANN-SNN转换算法，以解决可扩展鲁棒SNN训练方法进展有限的问题。该方法提供了一种高效途径，可整合针对ANN提出的各种计算要求严苛的鲁棒学习目标。在后转换鲁棒微调阶段，该方法通过对抗性优化逐层发放阈值和突触连接权重，维持预训练ANN传递的鲁棒性增益。我们在考虑SNN脉冲操作动态特性的多种自适应对抗场景下进行了实验评估，结果表明我们的方法为低延迟深度SNN提供了可扩展的最先进对抗鲁棒性解决方案。