mlsanityData to 7 Oct 2026

RSC-SNN: Exploring the Trade-off Between Adversarial Robustness and Accuracy in Spiking Neural Networks via Randomized Smoothing Coding

arXiv 2407.20099 ↗First posted 29 Jul 2024cs.CVcited by 5

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Abstract (arXiv)

Spiking Neural Networks (SNNs) have received widespread attention due to their unique neuronal dynamics and low-power nature. Previous research empirically shows that SNNs with Poisson coding are more robust than Artificial Neural Networks (ANNs) on small-scale datasets. However, it is still unclear in theory how the adversarial robustness of SNNs is derived, and whether SNNs can still maintain its adversarial robustness advantage on large-scale dataset tasks. This work theoretically demonstrates that SNN's inherent adversarial robustness stems from its Poisson coding. We reveal the conceptual equivalence of Poisson coding and randomized smoothing in defense strategies, and analyze in depth the trade-off between accuracy and adversarial robustness in SNNs via the proposed Randomized Smoothing Coding (RSC) method. Experiments demonstrate that the proposed RSC-SNNs show remarkable adversarial robustness, surpassing ANNs and achieving state-of-the-art robustness results on large-scale dataset ImageNet. Our open-source implementation code is available at this https URL: https://github.com/KemingWu/RSC-SNN.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 18 of its 47 references that are in the map. First posted on arXiv 29 Jul 2024.

Builds on

18 of 47 references.

References · 18

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  2. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  3. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  4. 2019Theoretically Principled Trade-off between Robustness and Accuracycited by 1,510
  5. 2019Certified Adversarial Robustness via Randomized Smoothingcited by 1,324
  6. 2018Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examplescited by 1,259
  7. 2017Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networkscited by 399
  8. 2021Deep Residual Learning in Spiking Neural Networkscited by 326
  9. 2020Going Deeper With Directly-Trained Larger Spiking Neural Networkscited by 268
  10. 2018Direct Training for Spiking Neural Networks: Faster, Larger, Bettercited by 222
  11. 2022Temporal Efficient Training of Spiking Neural Network via Gradient Re-weightingcited by 192
  12. 2018Is Robustness the Cost of Accuracy? -- A Comprehensive Study on the Robustness of 18 Deep Image Classification Modelscited by 135
6 more
  1. 2020MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radiuscited by 114
  2. 2021Accurate and efficient time-domain classification with adaptive spiking recurrent neural networkscited by 113
  3. 2021HIRE-SNN: Harnessing the Inherent Robustness of Energy-Efficient Deep Spiking Neural Networks by Training with Crafted Input Noisecited by 61
  4. 2023Speck: A Smart event-based Vision Sensor with a low latency 327K Neuron Convolutional Neuronal Network Processing Pipelinecited by 50
  5. 2020Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects of Discrete Input Encoding and Non-Linear Activationscited by 47
  6. 2021A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic Hardwarecited by 32

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Cited by year

Citing papers per year: 2024: 1; 2025: 4.

5 papers.

  1. 2024Scaling Spike-driven Transformer with Efficient Spike Firing Approximation Trainingcited by 57
  2. 2025Edge Intelligence with Spiking Neural Networkscited by 2
  3. 2025MPD-SGR: Robust Spiking Neural Networks with Membrane Potential Distribution-Driven Surrogate Gradient Regularizationcited by 2
  4. 2025On the Adversarial Robustness of Spiking Neural Networks Trained by Local Learningcited by 0
  5. 2025Boosting the Robustness-Accuracy Trade-off of SNNs by Robust Temporal Self-Ensemblecited by 0