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arXiv · 2604.01750

Spike-PTSD: A Bio-Plausible Adversarial Example Attack on Spiking Neural Networks via PTSD-Inspired Spike Scaling

Lingxin Jin·Wei Jiang·Maregu Assefa Habtie·Letian Chen·Jinyu Zhan·Xingzhi Zhou·Lin Zuo·Naoufel Werghi
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Abstract

Spiking Neural Networks (SNNs) are energy-efficient and biologically plausible, ideal for embedded and security-critical systems, yet their adversarial robustness remains open. Existing adversarial attacks often overlook SNNs' bio-plausible dynamics. We propose Spike-PTSD, a biologically inspired adversarial attack framework modeled on abnormal neural firing in Post-Traumatic Stress Disorder (PTSD). It localizes decision-critical layers, selects neurons via hyper/hypoactivation signatures, and optimizes adversarial examples with dual objectives. Across six datasets, three encoding types, and four models, Spike-PTSD achieves over 99% success rates, systematically compromising SNN robustness. Code: https://github.com/bluefier/Spike-PTSD.

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Lingxin Jin, Wei Jiang, Maregu Assefa Habtie, Letian Chen, Jinyu Zhan, Xingzhi Zhou, Lin Zuo, Naoufel Werghi. 2026-04-02. Spike-PTSD: A Bio-Plausible Adversarial Example Attack on Spiking Neural Networks via PTSD-Inspired Spike Scaling. https://arxiv.org/abs/2604.01750

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