mlsanityData to 14 Oct 2026

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

arXiv 2012.05321 ↗First posted 9 Dec 2020cs.LGcited by 27

Not on the map. Why?

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.

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

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 3; 2022: 4; 2023: 5; 2024: 3; 2025: 9; 2026: 3.

27 papers.

Sort:

By area:

  1. 2021HIRE-SNN: Harnessing the Inherent Robustness of Energy-Efficient Deep Spiking Neural Networks by Training with Crafted Input Noisecited by 65
  2. 2021DVS-Attacks: Adversarial Attacks on Dynamic Vision Sensors for Spiking Neural Networkscited by 21
  3. 2021Towards Energy-Efficient and Secure Edge AI: A Cross-Layer Frameworkcited by 17
  4. 2022Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examplescited by 13
  5. 2022Toward Robust Spiking Neural Network Against Adversarial Perturbationcited by 12
  6. 2025Towards Effective and Sparse Adversarial Attack on Spiking Neural Networks via Breaking Invisible Surrogate Gradientscited by 9
  7. 2023A Comprehensive Review of Spiking Neural Networks: Interpretation, Optimization, Efficiency, and Best Practicescited by 8
  8. 2024Advancing Training Efficiency of Deep Spiking Neural Networks through Rate-based Backpropagationcited by 8
  9. 2023Adversarially Robust Spiking Neural Networks Through Conversioncited by 7
  10. 2024Embodied Neuromorphic Artificial Intelligence for Robotics: Perspectives, Challenges, and Research Development Stackcited by 7
  11. 2022Special Session: Towards an Agile Design Methodology for Efficient, Reliable, and Secure ML Systemscited by 6
  12. 2025Efficient ANN-Guided Distillation: Aligning Rate-based Features of Spiking Neural Networks through Hybrid Block-wise Replacementcited by 6
15 more
  1. 2024Are Neuromorphic Architectures Inherently Privacy-preserving? An Exploratory Studycited by 5
  2. 2025Input-Triggered Hardware Trojan Attack on Spiking Neural Networkscited by 5
  3. 2023HoSNN: Adversarially-Robust Homeostatic Spiking Neural Networks with Adaptive Firing Thresholdscited by 3
  4. 2025MPD-SGR: Robust Spiking Neural Networks with Membrane Potential Distribution-Driven Surrogate Gradient Regularizationcited by 2
  5. 2022Adversarial Defense via Neural Oscillation inspired Gradient Maskingcited by 1
  6. 2025On the Privacy Risks of Spiking Neural Networks: A Membership Inference Analysiscited by 1
  7. 2025Input-Specific and Universal Adversarial Attack Generation for Spiking Neural Networks in the Spiking Domaincited by 1
  8. 2023Security-Aware Approximate Spiking Neural Networkscited by 0
  9. 2023HyperSNN: A new efficient and robust deep learning model for resource constrained control applicationscited by 0
  10. 2025Adversarially Robust Spiking Neural Networks with Sparse Connectivitycited by 0
  11. 2025Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directionscited by 0
  12. 2025TDSNNs: Competitive Topographic Deep Spiking Neural Networks for Visual Cortex Modelingcited by 0
  13. 2026Emerging Threats and Countermeasures in Neuromorphic Systems: A Surveycited by 0
  14. 2026Robust Spiking Neural Networks Against Adversarial Attackscited by 0
  15. 2026Driving up Inference Energy on SNNs: Per-Sample and Universal Sponge Attackscited by 0