Robust Semantic Communication Enabled by Spiking Neural Networks
The robustness of deep learning-based semantic communication systems remains a major challenge due to the vulnerability of artificial neural networks (ANNs) to adversarial attacks and the lack of clear robustness mechanisms in such models. Existing approaches often rely on data augmentation, which is training-intensive and requires prior knowledge of the perturbation distribution. In this paper, we instead leverage the structural properties of spiking neural networks (SNNs), a class of energy-efficient, brain-inspired models that compute using binary spikes. We exploit their inherent resilience enabled by the discrete and thresholded nature of spikes and analyze robustness from a structural perspective. For both random and adversarial perturbations, we identify two key factors: 1) membrane potentials should be kept away from the firing threshold to reduce spike flipping; and 2) synaptic weights should be regularized to prevent the amplification of perturbations across layers. Based on these observations, we propose robustness-aware potential (RAP), a simple regularizer designed to exploit the inherent robustness of SNNs. We evaluate our method on both digital and event-based datasets. Experimental results show that SNNs trained with RAP significantly improve robustness and exhibit better resilience to distributional shifts. Moreover, SNNs demonstrate a clear robustness advantage over ANNs, particularly under adversarial attacks.