mlsanityData to 7 Oct 2026

Toward Robust Spiking Neural Network Against Adversarial Perturbation

arXiv 2205.01625 ↗First posted 12 Apr 2022cs.NE cs.AI cs.CRcited by 12

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Abstract (arXiv)

As spiking neural networks (SNNs) are deployed increasingly in real-world efficiency critical applications, the security concerns in SNNs attract more attention. Currently, researchers have already demonstrated an SNN can be attacked with adversarial examples. How to build a robust SNN becomes an urgent issue. Recently, many studies apply certified training in artificial neural networks (ANNs), which can improve the robustness of an NN model promisely. However, existing certifications cannot transfer to SNNs directly because of the distinct neuron behavior and input formats for SNNs. In this work, we first design S-IBP and S-CROWN that tackle the non-linear functions in SNNs' neuron modeling. Then, we formalize the boundaries for both digital and spike inputs. Finally, we demonstrate the efficiency of our proposed robust training method in different datasets and model architectures. Based on our experiment, we can achieve a maximum 37.7% attack error reduction with 3.7% original accuracy loss. To the best of our knowledge, this is the first analysis on robust training of SNNs.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 24 of its 43 references that are in the map. First posted on arXiv 12 Apr 2022.

Builds on

24 of 43 references.

References · 24

  1. 2013Intriguing properties of neural networkscited by 5,114
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  3. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,976
  4. 2018Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examplescited by 1,259
  5. 2017Provable defenses against adversarial examples via the convex outer adversarial polytopecited by 564
  6. 2018Efficient Neural Network Robustness Certification with General Activation Functionscited by 439
  7. 2017Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networkscited by 399
  8. 2018On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Modelscited by 322
  9. 2015Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccadescited by 308
  10. 2021Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness Verificationcited by 224
  11. 2019Towards Stable and Efficient Training of Verifiably Robust Neural Networkscited by 204
  12. 2018Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networkscited by 193
12 more
  1. 2020Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifierscited by 183
  2. 2020Revisiting Batch Normalization for Training Low-latency Deep Spiking Neural Networks from Scratchcited by 76
  3. 2021HIRE-SNN: Harnessing the Inherent Robustness of Energy-Efficient Deep Spiking Neural Networks by Training with Crafted Input Noisecited by 61
  4. 2019A Comprehensive Analysis on Adversarial Robustness of Spiking Neural Networkscited by 35
  5. 2020Exploring Adversarial Attack in Spiking Neural Networks with Spike-Compatible Gradientcited by 31
  6. 2020Securing Deep Spiking Neural Networks against Adversarial Attacks through Inherent Structural Parameterscited by 27
  7. 2021Visual Explanations from Spiking Neural Networks using Interspike Intervalscited by 26
  8. 2022Rate Coding or Direct Coding: Which One is Better for Accurate, Robust, and Energy-efficient Spiking Neural Networks?cited by 24
  9. 2021DVS-Attacks: Adversarial Attacks on Dynamic Vision Sensors for Spiking Neural Networkscited by 21
  10. 2020Adversarial Training and Provable Robustness: A Tale of Two Objectivescited by 12
  11. 2021H2Learn: High-Efficiency Learning Accelerator for High-Accuracy Spiking Neural Networkscited by 7
  12. 2021Network insensitivity to parameter noise via adversarial regularizationcited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 3; 2026: 4.

12 papers.

  1. 2024Enhancing Adversarial Robustness in SNNs with Sparse Gradientscited by 9
  2. 2025Towards Effective and Sparse Adversarial Attack on Spiking Neural Networks via Breaking Invisible Surrogate Gradientscited by 9
  3. 2023Adversarially Robust Spiking Neural Networks Through Conversioncited by 7
  4. 2026Time Is All It Takes: Spike-Retiming Attacks on Event-Driven Spiking Neural Networkscited by 5
  5. 2023HoSNN: Adversarially-Robust Homeostatic Spiking Neural Networks with Adaptive Firing Thresholdscited by 3
  6. 2024Spiking GS: Towards High-Accuracy and Low-Cost Surface Reconstruction via Spiking Neuron-based Gaussian Splattingcited by 2
  7. 2025Edge Intelligence with Spiking Neural Networkscited by 2
  8. 2024Towards Efficient Formal Verification of Spiking Neural Networkcited by 0
  9. 2025Adversarially Robust Spiking Neural Networks with Sparse Connectivitycited by 0
  10. 2026Emerging Threats and Countermeasures in Neuromorphic Systems: A Surveycited by 0
  11. 2026Robust Spiking Neural Networks Against Adversarial Attackscited by 0
  12. 2026Benchmarking the Energy Cost of Assurance in Neuromorphic Edge Roboticscited by 0