# Watermarking Neuromorphic Brains: Intellectual Property Protection in Spiking Neural Networks

arXiv: https://arxiv.org/abs/2405.04049 · HTML: https://mlsanity.com/paper/2405.04049/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 30 references that are in the map.
- First posted on arXiv 7 May 2024.

## Builds on (most cited first, 16 of 16)

- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring](https://mlsanity.com/paper/1802.04633/) (2018)
- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
- [Embedding Watermarks into Deep Neural Networks](https://mlsanity.com/paper/1701.04082/) (2017)
- [Long short-term memory and learning-to-learn in networks of spiking neurons](https://mlsanity.com/paper/1803.09574/) (2018)
- [Adversarial Frontier Stitching for Remote Neural Network Watermarking](https://mlsanity.com/paper/1711.01894/) (2017)
- [DAWN: Dynamic Adversarial Watermarking of Neural Networks](https://mlsanity.com/paper/1906.00830/) (2019)
- [Deep Neural Network Fingerprinting by Conferrable Adversarial Examples](https://mlsanity.com/paper/1912.00888/) (2019)
- [SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks](https://mlsanity.com/paper/2302.13939/) (2023)
- [Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects of Discrete Input Encoding and Non-Linear Activations](https://mlsanity.com/paper/2003.10399/) (2020)
- [EV-IMO: Motion Segmentation Dataset and Learning Pipeline for Event Cameras](https://mlsanity.com/paper/1903.07520/) (2019)
- [Brain-Inspired Learning on Neuromorphic Substrates](https://mlsanity.com/paper/2010.11931/) (2020)
- [Intellectual Property Protection for Deep Learning Models: Taxonomy, Methods, Attacks, and Evaluations](https://mlsanity.com/paper/2011.13564/) (2020)
- [Deep Spiking Neural Networks for Large Vocabulary Automatic Speech Recognition](https://mlsanity.com/paper/1911.08373/) (2019)
- [Recent Advances and New Frontiers in Spiking Neural Networks](https://mlsanity.com/paper/2204.07050/) (2022)
- [BrainLeaks: On the Privacy-Preserving Properties of Neuromorphic Architectures against Model Inversion Attacks](https://mlsanity.com/paper/2402.00906/) (2024)

## Built on by (most cited first, 2 of 2)

- [Do Spikes Protect Privacy? Investigating Black-Box Model Inversion Attacks in Spiking Neural Networks](https://mlsanity.com/paper/2502.05509/) (2025)
- [MeMark: Membrane-Space Watermarking for Spiking Neural Networks](https://mlsanity.com/paper/2608.25738/) (2026)

## Abstract (arXiv)

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.