Are Spiking Neural Networks Harder to Steal? The Role of Stochastic Encoding in Model Extraction

arXiv:2610.36298v2 Announce Type: replace Abstract: Machine learning models deployed on edge devices are vulnerable to model stealing attacks, in which an adversary replicates a model using only its predictions. Existing defenses add mechanisms that are costly on resource-constrained hardware. Spiking neural networks (SNNs) offer energy-efficient inference at the edge and have been shown to leak less information about their training data than artificial neural networks (ANNs). This work investigates whether SNNs also resist model stealing and identifies the source of any such resistance. We ev

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