The content presents a novel approach to achieve adversarial robustness in spiking neural networks (SNNs) by converting adversarially trained artificial neural networks (ANNs) into SNNs. The key highlights are:
The authors propose an ANN-to-SNN conversion algorithm that initializes the SNN with weights from a robustly pre-trained baseline ANN, and then adversarially finetunes both the synaptic connectivity weights and the layer-wise firing thresholds of the SNN.
The method allows integrating any existing robust learning objective developed for conventional ANNs, such as TRADES or MART, into the ANN-to-SNN conversion process, thus optimally transferring robustness gains into the SNN.
The authors introduce a novel approach to incorporate adversarially pre-trained ANN batch-norm layer parameters within the spatio-temporal SNN batch-norm operations, without the need to omit these layers.
To rigorously evaluate SNN robustness, the authors propose an ensemble attack strategy that simulates adaptive adversaries based on different differentiable approximation techniques for the SNN's non-differentiable spike function.
Extensive experiments show that the proposed approach achieves a scalable state-of-the-art solution for adversarial robustness in deep SNNs, outperforming recently introduced end-to-end adversarial training based algorithms with up to 2× larger robustness gains and reduced robustness-accuracy trade-offs.
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