As spiking neural networks (SNNs) gain traction in deploying neuromorphic computing solutions, protecting their intellectual property (IP) has become crucial. Without adequate safeguards, proprietary SNN architectures are at risk of theft, replication, or misuse, which could lead to significant financial losses for the owners. While IP protection techniques have been extensively explored for artificial neural networks (ANNs), their applicability and effectiveness for the unique characteristics of SNNs remain largely unexplored. In this work, we pioneer an investigation into adapting two prominent watermarking approaches, namely, fingerprint-based and backdoor-based mechanisms to secure proprietary SNN architectures. We conduct thorough experiments to evaluate the impact on fidelity, resilience against overwrite threats, and resistance to compression attacks when applying these watermarking techniques to SNNs, drawing comparisons with their ANN counterparts. This study lays the groundwork for developing neuromorphic-aware IP protection strategies tailored to the distinctive dynamics of SNNs.

翻译：随着脉冲神经网络（SNNs）在部署神经形态计算解决方案中日益受到重视，保护其知识产权（IP）变得至关重要。若缺乏充分的安全措施，专有的SNN架构将面临被盗用、复制或滥用的风险，这可能导致所有者遭受重大经济损失。尽管人工神经网络（ANNs）的知识产权保护技术已被广泛研究，但这些技术对SNN独特特性的适用性和有效性在很大程度上仍未被探索。在本工作中，我们率先研究如何将两种主流水印方法（即基于指纹和基于后门的机制）应用于保护专有SNN架构。我们进行了全面的实验，评估在SNN上应用这些水印技术时对保真度的影响、对覆写威胁的鲁棒性以及对压缩攻击的抵抗性，并与对应的ANN方法进行了比较。这项研究为针对SNN独特动态特性制定神经形态感知的知识产权保护策略奠定了基础。