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Certified Adversarial Robustness for Rate Encoded Spiking Neural Networks

Bhaskar Mukhoty · Hilal AlQuabeh · Giulia De Masi · Huan Xiong · Bin Gu

Halle B #129
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Wed 8 May 7:30 a.m. PDT — 9:30 a.m. PDT

Abstract: The spiking neural networks are inspired by the biological neurons that employ binary spikes to propagate information in the neural network. It has garnered considerable attention as the next-generation neural network, as the spiking activity simplifies the computation burden of the network to a large extent and is known for its low energy deployment enabled by specialized neuromorphic hardware. One popular technique to feed a static image to such a network is rate encoding, where each pixel is encoded into random binary spikes, following a Bernoulli distribution that uses the pixel intensity as bias. By establishing a novel connection between rate-encoding and randomized smoothing, we give the first provable robustness guarantee for spiking neural networks against adversarial perturbation of inputs bounded under $l_1$-norm. We introduce novel adversarial training algorithms for rate-encoded models that significantly improve the state-of-the-art empirical robust accuracy result. Experimental validation of the method is performed across various static image datasets, including CIFAR-10, CIFAR-100 and ImageNet-100. The code is available at \url{}.

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