Spiking neural networks (SNNs) more closely mimic the human brain than artificial neural networks (ANNs). For SNNs, time-to-first-spike (TTFS) encoding, which represents the output values of neurons based on the timing of a single spike, has been proposed as a promising model to reduce power consumption. Adversarial attacks that can lead ANNs to misrecognize images have been reported in many studies. However, the characteristics of TTFS-based SNNs trained using a backpropagation algorithm against adversarial attacks have not yet been clarified. In particular, the dependence of the robustness against adversarial attacks on spike timings has not been investigated. In this brief, we investigated the robustness of SNNs against adversarial attacks and compared it with that of an ANN. We found that SNNs trained with the appropriate temporal penalty settings are more robust against adversarial images than ANNs.

中文翻译：

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基于 Time-to-First-Spike 编码的脉冲神经网络对抗对抗性攻击的鲁棒性

尖峰神经网络 (SNN) 比人工神经网络 (ANN) 更能模拟人脑。对于 SNN，首次尖峰时间 (TTFS) 编码表示基于单个尖峰时间的神经元的输出值，已被提出作为降低功耗的有前途的模型。许多研究报告了可能导致 ANN 错误识别图像的对抗性攻击。然而，使用反向传播算法训练的基于 TTFS 的 SNN 的特征尚未明确。特别是，尚未研究对抗性攻击的鲁棒性对尖峰时间的依赖性。在这篇简报中，我们研究了 SNN 对抗对抗性攻击的鲁棒性，并将其与 ANN 的鲁棒性进行了比较。