SNNs harder to steal: study
A new benchmarking study reveals that spiking neural networks (SNNs) are more resistant to model stealing attacks compared to conventional artificial neural networks (ANNs). The research, published in arXiv cs.AI, tested four representative model stealing attack methods against SNNs and their ANN counterparts on the MNIST, Fashion-MNIST, and CIFAR-10 datasets. Across all attacks and data sets,…
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Key points
- SNNs are more resistant to model stealing attacks
- Surrogates of SNNs show lower agreement with target models
- Resistance attributed to unique computing methods
Read the original at arXiv cs.AI · by Ayana Moshruba, Maryam Parsa primary sourceOpen source ↗
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