English(EN) BadSNN: Backdoor Attacks on Spiking Neural Networks via Adversarial Spiking Neuron

研究人员开发 BadSNN，利用脉冲神经元超参数进行后门攻击

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-05 04:00

研究人员开发了“BadSNN”，一种针对脉冲神经网络（SNN）的新型后门攻击。该攻击利用脉冲神经元超参数（如 Leaky Integrate-and-Fire 模型）的变异来引入恶意行为。BadSNN 在不同的数据集和架构上表现出强大的性能，优于现有的数据投毒攻击，并对常见的缓解策略表现出韧性。 AI

影响这项研究揭示了 SNN 的一种新漏洞，可能影响节能 AI 系统的安全性。

排序理由这是一篇详细介绍对脉冲神经网络进行新型后门攻击的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Abdullah Arafat Miah, Kevin Vu, Yu Bi · 2026-05-05 04:00

BadSNN：通过对抗性脉冲神经元对脉冲神经网络进行后门攻击

arXiv:2602.07200v2 Announce Type: replace-cross Abstract: Spiking Neural Networks (SNNs) are energy-efficient counterparts of Deep Neural Networks (DNNs) with high biological plausibility, as information is transmitted through temporal spiking patterns. The core element of an SNN…

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BadSNN：通过对抗性脉冲神经元对脉冲神经网络进行后门攻击

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