Spiking Neural Networks (SNNs) claim to present many advantages in terms of
biological plausibility and energy efficiency compared to standard Deep Neural
Networks (DNNs). Recent works have shown that DNNs are vulnerable to
adversarial attacks, i.e., small perturbations added to the input data can lead
to targeted or random misclassifications. In this paper, we aim at
investigating the key research question: ``Are SNNs secure?'' Towards this, we
perform a comparative study of the security vulnerabilities in SNNs and DNNs
w.r.t. the adversarial noise. Afterwards, we propose a novel black-box attack
methodology, i.e., without the knowledge of the internal structure of the SNN,
which employs a greedy heuristic to automatically generate imperceptible and
robust adversarial examples (i.e., attack images) for the given SNN. We perform
an in-depth evaluation for a Spiking Deep Belief Network (SDBN) and a DNN
having the same number of layers and neurons (to obtain a fair comparison), in
order to study the efficiency of our methodology and to understand the
differences between SNNs and DNNs w.r.t. the adversarial examples. Our work
opens new avenues of research towards the robustness of the SNNs, considering
their similarities to the human brain's functionality.

研究 Spiking Neural Networks（SNNs）和 Deep Neural Networks（DNNs）在对抗噪声方面的安全漏洞，并提出了一种新的黑盒攻击方法，考察了 SNNs 和 DNNs 之间在对抗性样本方面的差异，从而为研究 SNNs 的稳健性开辟了新的研究方向。