mlsanityData to 7 Oct 2026

Adversarially Robust Spiking Neural Networks Through Conversion

arXiv 2311.09266 ↗First posted 15 Nov 2023cs.NE cs.AI cs.LGcited by 7

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Abstract (arXiv)

Spiking neural networks (SNNs) provide an energy-efficient alternative to a variety of artificial neural network (ANN) based AI applications. As the progress in neuromorphic computing with SNNs expands their use in applications, the problem of adversarial robustness of SNNs becomes more pronounced. To the contrary of the widely explored end-to-end adversarial training based solutions, we address the limited progress in scalable robust SNN training methods by proposing an adversarially robust ANN-to-SNN conversion algorithm. Our method provides an efficient approach to embrace various computationally demanding robust learning objectives that have been proposed for ANNs. During a post-conversion robust finetuning phase, our method adversarially optimizes both layer-wise firing thresholds and synaptic connectivity weights of the SNN to maintain transferred robustness gains from the pre-trained ANN. We perform experimental evaluations in a novel setting proposed to rigorously assess the robustness of SNNs, where numerous adaptive adversarial attacks that account for the spike-based operation dynamics are considered. Results show that our approach yields a scalable state-of-the-art solution for adversarially robust deep SNNs with low-latency.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 62 of its 83 references that are in the map. First posted on arXiv 15 Nov 2023.

Builds on

62 of 83 references.

References · 62

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38 more
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Cited by year

Citing papers per year: 2024: 4; 2025: 3.

7 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. 2024Benchmarking Spiking Neural Network Learning Methods with Varying Localitycited by 2
  4. 2024Robust and Efficient Adversarial Defense in SNNs via Image Purification and Joint Detectioncited by 2
  5. 2024Towards Efficient Formal Verification of Spiking Neural Networkcited by 0
  6. 2025On the Adversarial Robustness of Spiking Neural Networks Trained by Local Learningcited by 0
  7. 2025Adversarially Robust Spiking Neural Networks with Sparse Connectivitycited by 0