Spiking neural networks (SNNs) have attracted much attention for their high energy efficiency and for recent advances in their classification performance. However, unlike traditional deep learning approaches, the analysis and study of the robustness of SNNs to adversarial examples remains relatively underdeveloped. In this work we advance the field of adversarial machine learning through experimentation and analyses of three important SNN security attributes. First, we show that successful white-box adversarial attacks on SNNs are highly dependent on the underlying surrogate gradient technique. Second, we analyze the transferability of adversarial examples generated by SNNs and other state-of-the-art architectures like Vision Transformers and Big Transfer CNNs. We demonstrate that SNNs are not often deceived by adversarial examples generated by Vision Transformers and certain types of CNNs. Lastly, we develop a novel white-box attack that generates adversarial examples capable of fooling both SNN models and non-SNN models simultaneously. Our experiments and analyses are broad and rigorous covering two datasets (CIFAR-10 and CIFAR-100), five different white-box attacks and twelve different classifier models.

翻译：在这项工作中,我们通过试验和对三个重要的SNN安全属性的分析,推进对抗机器学习领域。首先,我们表明,对SNN的成功白箱对抗性攻击高度依赖潜在的代用梯度技术。第二,我们分析SNN和其他最先进的结构,如愿景变换器和大型转移式CNN等的对抗性例子的可转移性。我们证明,SNN往往不会被愿景变换器和某些类型的CNN产生的对抗性例子所欺骗。最后,我们开发了一种新的白箱攻击,产生能够同时愚弄SNN模型和非SNN模型的对抗性例子。我们的实验和分析广泛而严格,涵盖了两个数据集(CIFAR-10和CIFAR-100)、五个不同的白箱攻击和12个不同的分类模型。