Toward Robust Spiking Neural Network Against Adversarial Perturbation. (arXiv:2205.01625v1 [cs.NE])

As spiking neural networks (SNNs) are deployed increasingly in real-world
efficiency critical applications, the security concerns in SNNs attract more
attention. Currently, researchers have already demonstrated an SNN can be
attacked with adversarial examples. How to build a robust SNN becomes an urgent
issue. Recently, many studies apply certified training in artificial neural
networks (ANNs), which can improve the robustness of an NN model promisely.
However, existing certifications cannot transfer to SNNs directly because of
the distinct neuron behavior and …

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