Watermarking Neuromorphic Brains: Intellectual Property Protection in Spiking Neural Networks
This work addresses the risk of theft and misuse of proprietary SNN architectures for owners in neuromorphic computing, but it is incremental as it adapts existing methods to a new domain.
The paper tackles the problem of intellectual property protection for spiking neural networks (SNNs) by adapting fingerprint-based and backdoor-based watermarking techniques, evaluating their impact on fidelity, resilience, and resistance to attacks with comparisons to artificial neural networks (ANNs).
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.