Adversarial Training for Probabilistic Spiking Neural Networks
This addresses the vulnerability of SNNs to adversarial attacks, which is an incremental step in enhancing robustness for neuromorphic computing applications.
The paper studied the sensitivity of spiking neural networks (SNNs) to adversarial examples for the first time, considering various encoding and decoding methods, and proposed a robust training mechanism that improved SNN performance under white-box attacks.
Classifiers trained using conventional empirical risk minimization or maximum likelihood methods are known to suffer dramatic performance degradations when tested over examples adversarially selected based on knowledge of the classifier's decision rule. Due to the prominence of Artificial Neural Networks (ANNs) as classifiers, their sensitivity to adversarial examples, as well as robust training schemes, have been recently the subject of intense investigation. In this paper, for the first time, the sensitivity of spiking neural networks (SNNs), or third-generation neural networks, to adversarial examples is studied. The study considers rate and time encoding, as well as rate and first-to-spike decoding. Furthermore, a robust training mechanism is proposed that is demonstrated to enhance the performance of SNNs under white-box attacks.