@inproceedings{e1c17e64d08a414d8c45a19415330da2,
title = "Can invisible physical events easily fool spiking neural networks?",
abstract = "Event-based dynamic vision sensors, which generate sparse spike-based outputs, are ideal for low-power applications. Spiking Neural Networks are designed to process this data efficiently on asynchronous neuromorphic hardware. As event-based vision advances, understanding the vulnerability of Spiking Neural Networks to physical adversarial attacks becomes crucial. This work introduces a novel light-based adversarial attack on neuromorphic vision. We exploit undetectable optical events, specifically designed light pulses, to disrupt the temporal dynamics of event-based sensors. Our method demonstrates how these physical attacks can be tailored to the event-based data's discrete and sparse nature while achieving high success rates.",
keywords = "Dynamicvision sensors, Neuromorphic vision, Optical perturbations, Physical adversarial events, Robust AI, Spiking neural networks",
author = "Adir Hazan and Ido Avrahami and Adrian Stern",
note = "Publisher Copyright: {\textcopyright} COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.; 3rd Artificial Intelligence for Security and Defence Applications ; Conference date: 16-09-2025 Through 18-09-2025",
year = "2025",
month = oct,
day = "28",
doi = "10.1117/12.3070058",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Kuijf, \{Hugo J.\} and Radhakrishna Prabhu and Yitzhak Yitzhaky",
booktitle = "Artificial Intelligence for Security and Defence Applications III",
address = "United States",
}