# Securing Deep Spiking Neural Networks against Adversarial Attacks through Inherent Structural Parameters

arXiv: https://arxiv.org/abs/2012.05321 · HTML: https://mlsanity.com/paper/2012.05321/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2020.

## Built on by (most cited first, 27 of 27)

- [HIRE-SNN: Harnessing the Inherent Robustness of Energy-Efficient Deep Spiking Neural Networks by Training with Crafted Input Noise](https://mlsanity.com/paper/2110.11417/) (2021)
- [DVS-Attacks: Adversarial Attacks on Dynamic Vision Sensors for Spiking Neural Networks](https://mlsanity.com/paper/2107.00415/) (2021)
- [Towards Energy-Efficient and Secure Edge AI: A Cross-Layer Framework](https://mlsanity.com/paper/2109.09829/) (2021)
- [Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples](https://mlsanity.com/paper/2209.03358/) (2022)
- [Toward Robust Spiking Neural Network Against Adversarial Perturbation](https://mlsanity.com/paper/2205.01625/) (2022)
- [Towards Effective and Sparse Adversarial Attack on Spiking Neural Networks via Breaking Invisible Surrogate Gradients](https://mlsanity.com/paper/2503.03272/) (2025)
- [A Comprehensive Review of Spiking Neural Networks: Interpretation, Optimization, Efficiency, and Best Practices](https://mlsanity.com/paper/2303.10780/) (2023)
- [Advancing Training Efficiency of Deep Spiking Neural Networks through Rate-based Backpropagation](https://mlsanity.com/paper/2410.11488/) (2024)
- [Adversarially Robust Spiking Neural Networks Through Conversion](https://mlsanity.com/paper/2311.09266/) (2023)
- [Embodied Neuromorphic Artificial Intelligence for Robotics: Perspectives, Challenges, and Research Development Stack](https://mlsanity.com/paper/2404.03325/) (2024)
- [Special Session: Towards an Agile Design Methodology for Efficient, Reliable, and Secure ML Systems](https://mlsanity.com/paper/2204.09514/) (2022)
- [Efficient ANN-Guided Distillation: Aligning Rate-based Features of Spiking Neural Networks through Hybrid Block-wise Replacement](https://mlsanity.com/paper/2503.16572/) (2025)
- [Are Neuromorphic Architectures Inherently Privacy-preserving? An Exploratory Study](https://mlsanity.com/paper/2411.06613/) (2024)
- [Input-Triggered Hardware Trojan Attack on Spiking Neural Networks](https://mlsanity.com/paper/2503.21793/) (2025)
- [HoSNN: Adversarially-Robust Homeostatic Spiking Neural Networks with Adaptive Firing Thresholds](https://mlsanity.com/paper/2308.10373/) (2023)
- [MPD-SGR: Robust Spiking Neural Networks with Membrane Potential Distribution-Driven Surrogate Gradient Regularization](https://mlsanity.com/paper/2511.12199/) (2025)
- [Adversarial Defense via Neural Oscillation inspired Gradient Masking](https://mlsanity.com/paper/2211.02223/) (2022)
- [On the Privacy Risks of Spiking Neural Networks: A Membership Inference Analysis](https://mlsanity.com/paper/2502.13191/) (2025)
- [Input-Specific and Universal Adversarial Attack Generation for Spiking Neural Networks in the Spiking Domain](https://mlsanity.com/paper/2505.06299/) (2025)
- [Security-Aware Approximate Spiking Neural Networks](https://mlsanity.com/paper/2301.05264/) (2023)
- [HyperSNN: A new efficient and robust deep learning model for resource constrained control applications](https://mlsanity.com/paper/2308.08222/) (2023)
- [Adversarially Robust Spiking Neural Networks with Sparse Connectivity](https://mlsanity.com/paper/2505.15833/) (2025)
- [Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions](https://mlsanity.com/paper/2507.18139/) (2025)
- [TDSNNs: Competitive Topographic Deep Spiking Neural Networks for Visual Cortex Modeling](https://mlsanity.com/paper/2508.04270/) (2025)
- [Emerging Threats and Countermeasures in Neuromorphic Systems: A Survey](https://mlsanity.com/paper/2601.16589/) (2026)
- [Robust Spiking Neural Networks Against Adversarial Attacks](https://mlsanity.com/paper/2602.20548/) (2026)
- [Driving up Inference Energy on SNNs: Per-Sample and Universal Sponge Attacks](https://mlsanity.com/paper/2607.27990/) (2026)

## Abstract (arXiv)

Deep Learning (DL) algorithms have gained popularity owing to their practical problem-solving capacity. However, they suffer from a serious integrity threat, i.e., their vulnerability to adversarial attacks. In the quest for DL trustworthiness, recent works claimed the inherent robustness of Spiking Neural Networks (SNNs) to these attacks, without considering the variability in their structural spiking parameters. This paper explores the security enhancement of SNNs through internal structural parameters. Specifically, we investigate the SNNs robustness to adversarial attacks with different values of the neuron's firing voltage thresholds and time window boundaries. We thoroughly study SNNs security under different adversarial attacks in the strong white-box setting, with different noise budgets and under variable spiking parameters. Our results show a significant impact of the structural parameters on the SNNs' security, and promising sweet spots can be reached to design trustworthy SNNs with 85% higher robustness than a traditional non-spiking DL system. To the best of our knowledge, this is the first work that investigates the impact of structural parameters on SNNs robustness to adversarial attacks. The proposed contributions and the experimental framework is available online to the community for reproducible research.