# Is Spiking Secure? A Comparative Study on the Security Vulnerabilities of Spiking and Deep Neural Networks

## TL;DR

This paper compares the security vulnerabilities of Spiking Neural Networks (SNNs) and Deep Neural Networks (DNNs), introduces a novel black-box attack method for SNNs, and evaluates their robustness against adversarial noise.

## Contribution

It provides the first comparative analysis of SNN and DNN vulnerabilities and proposes a new black-box attack technique for SNNs.

## Key findings

- SNNs exhibit different vulnerability patterns compared to DNNs.
- The proposed attack effectively generates imperceptible adversarial examples for SNNs.
- Insights into the robustness of SNNs suggest new directions for secure neural network design.

## Abstract

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.