  In this era of machine learning models, their functionality is being
threatened by adversarial attacks. In the face of this struggle for making
artificial neural networks robust, finding a model, resilient to these attacks,
is very important. In this work, we present, for the first time, a
comprehensive analysis of the behavior of more bio-plausible networks, namely
Spiking Neural Network (SNN) under state-of-the-art adversarial tests. We
perform a comparative study of the accuracy degradation between conventional
VGG-9 Artificial Neural Network (ANN) and equivalent spiking network with
CIFAR-10 dataset in both whitebox and blackbox setting for different types of
single-step and multi-step FGSM (Fast Gradient Sign Method) attacks. We
demonstrate that SNNs tend to show more resiliency compared to ANN under
black-box attack scenario. Additionally, we find that SNN robustness is largely
dependent on the corresponding training mechanism. We observe that SNNs trained
by spike-based backpropagation are more adversarially robust than the ones
obtained by ANN-to-SNN conversion rules in several whitebox and blackbox
scenarios. Finally, we also propose a simple, yet, effective framework for
crafting adversarial attacks from SNNs. Our results suggest that attacks
crafted from SNNs following our proposed method are much stronger than those
crafted from ANNs.
