针对故障攻击的人工神经网络智能冗余方案

Troya Çağıl Köylü, S. Hamdioui, M. Taouil
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引用次数: 1

摘要

人工神经网络(ann)用于完成各种任务,包括安全关键任务。因此,重要的是要保护它们免受可能在操作期间影响决策的故障的影响。在本文中,我们提出了智能和低成本的冗余方案,以保护最脆弱的人工神经网络部分免受故障攻击。实验结果表明,这两种智能方案的性能与双模冗余(DMR)相似,成本更低,总体上改进了目前的技术水平,并达到了93%至99%的保护水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Smart Redundancy Schemes for ANNs Against Fault Attacks
Artificial neural networks (ANNs) are used to accomplish a variety of tasks, including safety critical ones. Hence, it is important to protect them against faults that can influence decisions during operation. In this paper, we propose smart and low-cost redundancy schemes that protect the most vulnerable ANN parts against fault attacks. Experimental results show that the two proposed smart schemes perform similarly to dual modular redundancy (DMR) at a much lower cost, generally improve on the state of the art, and reach protection levels in the range of 93% to 99%.
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