Threaten Spiking Neural Networks through Combining Rate and Temporal Information

Published: 16 Jan 2024, Last Modified: 21 Apr 2024ICLR 2024 posterEveryoneRevisionsBibTeX
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.
Keywords: Spiking Neural Networks, Rate and Temporal Information, Hybrid Adversarial Attack
Submission Guidelines: I certify that this submission complies with the submission instructions as described on
TL;DR: We propose a hybrid adversarial attack based on rate and temporal information (HART) for Spiking Neural Networks, whose rate and temporal attribute are dynamically adjustable.
Abstract: Spiking Neural Networks (SNNs) have received widespread attention in academic communities due to their superior spatio-temporal processing capabilities and energy-efficient characteristics. With further in-depth application in various fields, the vulnerability of SNNs under adversarial attack has become a focus of concern. In this paper, we draw inspiration from two mainstream learning algorithms of SNNs and observe that SNN models reserve both rate and temporal information. To better understand the capabilities of these two types of information, we conduct a quantitative analysis separately for each. In addition, we note that the retention degree of temporal information is related to the parameters and input settings of spiking neurons. Building on these insights, we propose a hybrid adversarial attack based on rate and temporal information (HART), which allows for dynamic adjustment of the rate and temporal attributes. Experimental results demonstrate that compared to previous works, HART attack can achieve significant superiority under different attack scenarios, data types, network architecture, time-steps, and model hyper-parameters. These findings call for further exploration into how both types of information can be effectively utilized to enhance the reliability of SNNs. Code is available at [](
Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors' identity.
Supplementary Material: zip
No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.
Primary Area: applications to neuroscience & cognitive science
Submission Number: 56