Sparse Gradient Training for Robustness Transferability Across Adversarial Norms in Multimodal Spiking Neural Networks

Abstract

Abstract: Spiking Neural Networks (SNNs) have attracted great attention for their energy-efficient operations and biologically inspired structures, offering potential advantages over Artificial Neural Networks (ANNs) in terms of energy efficiency and interpretability. Nonetheless, similar to ANNs, the robustness of SNNs remains a challenge, especially when facing adversarial attacks. Existing techniques, whether adapted from ANNs or specifically designed for SNNs, exhibit limitations in training SNNs or defending against strong attacks. In this paper, we propose a novel approach to enhance the robustnes

Research Question

Can sparse gradient training techniques for SNNs improve robustness transferability across different adversarial perturbation norms on multimodal benchmarks?

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