  Spiking Neural Networks (SNNs) have received widespread attention due to
their unique neuronal dynamics and low-power nature. Previous research
empirically shows that SNNs with Poisson coding are more robust than Artificial
Neural Networks (ANNs) on small-scale datasets. However, it is still unclear in
theory how the adversarial robustness of SNNs is derived, and whether SNNs can
still maintain its adversarial robustness advantage on large-scale dataset
tasks. This work theoretically demonstrates that SNN's inherent adversarial
robustness stems from its Poisson coding. We reveal the conceptual equivalence
of Poisson coding and randomized smoothing in defense strategies, and analyze
in depth the trade-off between accuracy and adversarial robustness in SNNs via
the proposed Randomized Smoothing Coding (RSC) method. Experiments demonstrate
that the proposed RSC-SNNs show remarkable adversarial robustness, surpassing
ANNs and achieving state-of-the-art robustness results on large-scale dataset
ImageNet. Our open-source implementation code is available at this https URL:
https://github.com/KemingWu/RSC-SNN.
