Exploring Adversarial Attack in Spiking Neural Networks with Spike-Compatible Gradient

1 Jan 2020Ling LiangXing HuLei DengYujie WuGuoqi LiYufei DingPeng LiYuan Xie

Recently, backpropagation through time inspired learning algorithms are widely introduced into SNNs to improve the performance, which brings the possibility to attack the models accurately given Spatio-temporal gradient maps. We propose two approaches to address the challenges of gradient input incompatibility and gradient vanishing... (read more)

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