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Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples

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arXiv:2209.03358v5 Announce Type: replace Abstract: Spiking neural networks (SNNs) have attracted much attention for their high energy efficiency and recent advances in classification performance. However, unlike traditional deep learning approaches, the study of SNN robustness to adversarial exampl

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