Adversarial Attacks on Spiking Convolutional Neural Networks for Event-based Vision

TL;DR

This paper investigates the vulnerability of spiking convolutional neural networks for event-based vision to adversarial attacks, demonstrating effective attack methods and defenses on neuromorphic hardware.

Contribution

It adapts white-box adversarial attack algorithms to event-based data and verifies their effectiveness directly on neuromorphic hardware, a first in the field.

Findings

Higher success rates with smaller perturbations compared to state-of-the-art

Effective adversarial attacks demonstrated on neuromorphic hardware

Adversarial training as a potential defense strategy

Abstract

Event-based dynamic vision sensors provide very sparse output in the form of spikes, which makes them suitable for low-power applications. Convolutional spiking neural networks model such event-based data and develop their full energy-saving potential when deployed on asynchronous neuromorphic hardware. Event-based vision being a nascent field, the sensitivity of spiking neural networks to potentially malicious adversarial attacks has received little attention so far. We show how white-box adversarial attack algorithms can be adapted to the discrete and sparse nature of event-based visual data, and demonstrate smaller perturbation magnitudes at higher success rates than the current state-of-the-art algorithms. For the first time, we also verify the effectiveness of these perturbations directly on neuromorphic hardware. Finally, we discuss the properties of the resulting perturbations, the effect of adversarial training as a defense strategy, and future directions.