  Low-latency deep spiking neural networks (SNNs) have become a promising
alternative to conventional artificial neural networks (ANNs) because of their
potential for increased energy efficiency on event-driven neuromorphic
hardware. Neural networks, including SNNs, however, are subject to various
adversarial attacks and must be trained to remain resilient against such
attacks for many applications. Nevertheless, due to prohibitively high training
costs associated with SNNs, analysis, and optimization of deep SNNs under
various adversarial attacks have been largely overlooked. In this paper, we
first present a detailed analysis of the inherent robustness of low-latency
SNNs against popular gradient-based attacks, namely fast gradient sign method
(FGSM) and projected gradient descent (PGD). Motivated by this analysis, to
harness the model robustness against these attacks we present an SNN training
algorithm that uses crafted input noise and incurs no additional training time.
To evaluate the merits of our algorithm, we conducted extensive experiments
with variants of VGG and ResNet on both CIFAR-10 and CIFAR-100 datasets.
Compared to standard trained direct input SNNs, our trained models yield
improved classification accuracy of up to 13.7% and 10.1% on FGSM and PGD
attack-generated images, respectively, with negligible loss in clean image
accuracy. Our models also outperform inherently robust SNNs trained on
rate-coded inputs with improved or similar classification performance on
attack-generated images while having up to 25x and 4.6x lower latency and
computation energy, respectively.

Computer Vision and Pattern Recognition;Artificial Intelligence