发表机构
University of Rhode Island（罗德岛大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对SNN后门攻击中时空触发器易被检测的问题，提出仅用时间触发器（速率、延迟、抖动）的T-Backdoor，在三个神经形态数据集上实现100%攻击成功率且对七种防御鲁棒。
AI 中文摘要
后门攻击是对深度神经网络（DNN）的严重安全威胁，而对于脉冲神经网络（SNN）的此类攻击在很大程度上仍未得到充分探索。现有攻击主要引入时空触发器，导致中毒样本的脉冲分布相对于其干净对应样本产生偏差。为解决这一局限，本工作提出了一种针对SNN的新型后门攻击，称为T-Backdoor，它仅使用纯时间触发器，如速率（Rate）、延迟（Latency）和抖动（Jitter），而不引入任何空间扰动，使得脉冲分布的偏移显著更难被检测。通过在三个基准神经形态数据集：N-MNIST、CIFAR10-DVS和N-Caltech101上进行的大量实验，并针对七种基线后门防御方法进行评估，我们证明T-Backdoor在单目标和多目标设置下均能达到接近完美的100%攻击成功率（ASR），且仅造成轻微的清准确率下降，同时对现有的后门检测和缓解技术保持鲁棒性。代码可在以下https URL获取。
英文摘要
Backdoor attacks are a serious security threat to deep neural networks (DNNs) and remain largely underexplored for spiking neural networks (SNNs). Existing attacks primarily introduce spatiotemporal triggers that induce deviations in the spike distribution of poisoned samples relative to their clean counterparts. To address this limitation, this work proposes a novel backdoor attack on SNNs, termed \textbf{T-Backdoor}, which operates using purely temporal triggers such as \textit{Rate}, \textit{Latency}, and \textit{Jitter} without introducing any spatial perturbation, making the shift in spike distributions significantly harder to detect. Through extensive experiments on three benchmark neuromorphic datasets: N-MNIST, CIFAR10-DVS, and N-Caltech101, and evaluation against seven baseline backdoor defense methods, we demonstrate that T-Backdoor achieves a near-perfect 100\% attack success rate (ASR) in both single target and multi target settings with only minor degradation in clean accuracy, while remaining robust against existing backdoor detection and mitigation techniques. The codes are available at https://github.com/SiSL-URI/T-Backdoor .
Comments14 pages, 12 figures
