In the recent quest for trustworthy neural networks, we present Spiking
Neural Network (SNN) as a potential candidate for inherent robustness against
adversarial attacks. In this work, we demonstrate that adversarial accuracy of
SNNs under gradient-based attacks is higher than their non-spiking counterparts
for CIFAR datasets on deep VGG and ResNet architectures, particularly in
blackbox attack scenario. We attribute this robustness to two fundamental
characteristics of SNNs and analyze their effects. First, we exhibit that input
discretization introduced by the Poisson encoder improves adversarial
robustness with reduced number of timesteps. Second, we quantify the amount of
adversarial accuracy with increased leak rate in Leaky-Integrate-Fire (LIF)
neurons. Our results suggest that SNNs trained with LIF neurons and smaller
number of timesteps are more robust than the ones with IF (Integrate-Fire)
neurons and larger number of timesteps. Also we overcome the bottleneck of
creating gradient-based adversarial inputs in temporal domain by proposing a
technique for crafting attacks from SNN

本文介绍了潜在的对抗攻击抗干扰能力强的脉冲神经网络（SNN），以及研究表明使用 Poisson 编码器的输入离散化和 Leaky-Integrate-Fire (LIF) 神经元的泄漏率增加可提高其在 CIFAR 数据集上的对抗准确性。