mlsanityData to 7 Oct 2026

R-SNN: An Analysis and Design Methodology for Robustifying Spiking Neural Networks against Adversarial Attacks through Noise Filters for Dynamic Vision Sensors

arXiv 2109.00533 ↗First posted 1 Sep 2021cs.LG cs.CR cs.NEcited by 9

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Abstract (arXiv)

Spiking Neural Networks (SNNs) aim at providing energy-efficient learning capabilities when implemented on neuromorphic chips with event-based Dynamic Vision Sensors (DVS). This paper studies the robustness of SNNs against adversarial attacks on such DVS-based systems, and proposes R-SNN, a novel methodology for robustifying SNNs through efficient DVS-noise filtering. We are the first to generate adversarial attacks on DVS signals (i.e., frames of events in the spatio-temporal domain) and to apply noise filters for DVS sensors in the quest for defending against adversarial attacks. Our results show that the noise filters effectively prevent the SNNs from being fooled. The SNNs in our experiments provide more than 90% accuracy on the DVS-Gesture and NMNIST datasets under different adversarial threat models.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 15 of its 29 references that are in the map. First posted on arXiv 1 Sep 2021.

Builds on

15 of 29 references.

References · 15

  1. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  2. 2019Theoretically Principled Trade-off between Robustness and Accuracycited by 1,510
  3. 2015Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccadescited by 308
  4. 2018SLAYER: Spike Layer Error Reassignment in Timecited by 257
  5. 2018Sparse Adversarial Perturbations for Videoscited by 56
  6. 2021Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Aheadcited by 40
  7. 2019A Comprehensive Analysis on Adversarial Robustness of Spiking Neural Networkscited by 35
  8. 2020Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Aheadcited by 34
  9. 2020Exploring Adversarial Attack in Spiking Neural Networks with Spike-Compatible Gradientcited by 31
  10. 2020NeuroAttack: Undermining Spiking Neural Networks Security through Externally Triggered Bit-Flipscited by 21
  11. 2018Adversarial Training for Probabilistic Spiking Neural Networkscited by 11
  12. 2019Deep SCNN-based Real-time Object Detection for Self-driving Vehicles Using LiDAR Temporal Datacited by 8
3 more
  1. 2017Neural Networks for Safety-Critical Applications - Challenges, Experiments and Perspectivescited by 7
  2. 2018Gridbot: An autonomous robot controlled by a Spiking Neural Network mimicking the brain's navigational systemcited by 7
  3. 2018FAdeML: Understanding the Impact of Pre-Processing Noise Filtering on Adversarial Machine Learningcited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 2; 2025: 2; 2026: 1.

9 papers.

  1. 2021Towards Energy-Efficient and Secure Edge AI: A Cross-Layer Frameworkcited by 17
  2. 2022LaneSNNs: Spiking Neural Networks for Lane Detection on the Loihi Neuromorphic Processorcited by 10
  3. 2024Enhancing Adversarial Robustness in SNNs with Sparse Gradientscited by 9
  4. 2025Towards Effective and Sparse Adversarial Attack on Spiking Neural Networks via Breaking Invisible Surrogate Gradientscited by 9
  5. 2024Embodied Neuromorphic Artificial Intelligence for Robotics: Perspectives, Challenges, and Research Development Stackcited by 7
  6. 2022Special Session: Towards an Agile Design Methodology for Efficient, Reliable, and Secure ML Systemscited by 6
  7. 2023Security-Aware Approximate Spiking Neural Networkscited by 0
  8. 2025Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directionscited by 0
  9. 2026Benchmarking the Energy Cost of Assurance in Neuromorphic Edge Roboticscited by 0