@inproceedings{841d806114ac46f5a493bcc34781bbe7,
title = "R-SNN: An Analysis and Design Methodology for Robustifying Spiking Neural Networks against Adversarial Attacks through Noise Filters for Dynamic Vision Sensors",
abstract = "Spiking Neural Networks (SNNs) aim at providing energy-efficient learning capabilities when implemented on neuromorphic chips with event-based Dynamic Vision Sensors (DVS). This paper studies the robustness of SNNs against adversarial attacks on such DVS-based systems, and proposes R-SNN, a novel methodology for robustifying SNNs through efficient DVS-noise filtering. We are the first to generate adversarial attacks on DVS signals (i.e., frames of events in the spatio-temporal domain) and to apply noise filters for DVS sensors in the quest for defending against adversarial attacks. Our results show that the noise filters effectively prevent the SNNs from being fooled. The SNNs in our experiments provide more than 90% accuracy on the DVS-Gesture and NMNIST datasets under different adversarial threat models. ",
keywords = "Adversarial Attacks, Deep Learning, Defense, DVS, DVS-Gesture, Dynamic Vision Sensors, Event-Based, Filter, Neuromorphic, NMNIST, Noise, Perturbation, Robustness, Security, SNNs, Spiking Neural Networks",
author = "Alberto Marchisio and Giacomo Pira and Maurizio Martina and Guido Masera and Muhammad Shafique",
note = "Funding Information: This work has been partially supported by the Doctoral College Resilient Embedded Systems, which is run jointly by the TU Wien{\textquoteright}s Faculty of Informatics and the UAS Technikum Wien. This work was also jointly supported by the NYUAD Center for Interacting Urban Networks (CITIES), funded by Tamkeen under the NYUAD Research Institute Award CG001 and by the Swiss Re Institute under the Quantum Cities{\texttrademark} initiative, and Center for CyberSecurity (CCS), funded by Tamkeen under the NYUAD Research Institute Award G1104. Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021 ; Conference date: 27-09-2021 Through 01-10-2021",
year = "2021",
doi = "10.1109/IROS51168.2021.9636718",
language = "English (US)",
series = "IEEE International Conference on Intelligent Robots and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6315--6321",
booktitle = "IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021",
}