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IEEE/ICACT20230030 Slide.03        [Big Slide]	Oral Presentation
	In 2015, J. Yang [17] et al. demonstrated that the classification capability of SNNs is robust to sinusoidal and Gaussian noise of the input signal. In 2019, it has been demonstrated that SNNs are more resilient than ANNs under black-box attacks [19]. In 2021, R. El-Allami et al. [6] investigated the effect of different structural parameters (e.g. threshold voltage, time step) on the robustness of SNN models under white-box attacks based on previous experiments, and the experimental results showed that SNNs improved robustness by 85% over ANNs for specific parameters. The SNN is thus shown to have the potential to replace ANNs in noisy inputs scenarios.

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