# Boosting the Robustness-Accuracy Trade-off of SNNs by Robust Temporal Self-Ensemble

arXiv: https://arxiv.org/abs/2508.11279 · HTML: https://mlsanity.com/paper/2508.11279/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 30 references that are in the map.
- First posted on arXiv 15 Aug 2025.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks](https://mlsanity.com/paper/2003.01690/) (2020)
- [Deep Residual Learning in Spiking Neural Networks](https://mlsanity.com/paper/2102.04159/) (2021)
- [Improving Adversarial Robustness via Promoting Ensemble Diversity](https://mlsanity.com/paper/1901.08846/) (2019)
- [Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting](https://mlsanity.com/paper/2202.11946/) (2022)
- [DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles](https://mlsanity.com/paper/2009.14720/) (2020)
- [Exploring Adversarial Attack in Spiking Neural Networks with Spike-Compatible Gradient](https://mlsanity.com/paper/2001.01587/) (2020)
- [Understanding and Improving Ensemble Adversarial Defense](https://mlsanity.com/paper/2310.18477/) (2023)
- [FireFly: A High-Throughput Hardware Accelerator for Spiking Neural Networks with Efficient DSP and Memory Optimization](https://mlsanity.com/paper/2301.01905/) (2023)
- [Rethinking Spiking Neural Networks from an Ensemble Learning Perspective](https://mlsanity.com/paper/2502.14218/) (2025)
- [Improving Stability and Performance of Spiking Neural Networks through Enhancing Temporal Consistency](https://mlsanity.com/paper/2305.14174/) (2023)
- [Enhancing Adversarial Robustness in SNNs with Sparse Gradients](https://mlsanity.com/paper/2405.20355/) (2024)
- [Towards Effective and Sparse Adversarial Attack on Spiking Neural Networks via Breaking Invisible Surrogate Gradients](https://mlsanity.com/paper/2503.03272/) (2025)
- [RSC-SNN: Exploring the Trade-off Between Adversarial Robustness and Accuracy in Spiking Neural Networks via Randomized Smoothing Coding](https://mlsanity.com/paper/2407.20099/) (2024)

## Built on by (most cited first, 1 of 1)

- [Controllable Stochastic Quantization Encoding for Adversarially Robust Spiking Neural Networks](https://mlsanity.com/paper/2610.01558/) (2026)

## Abstract (arXiv)

Spiking Neural Networks (SNNs) offer a promising direction for energy-efficient and brain-inspired computing, yet their vulnerability to adversarial perturbations remains poorly understood. In this work, we revisit the adversarial robustness of SNNs through the lens of temporal ensembling, treating the network as a collection of evolving sub-networks across discrete timesteps. This formulation uncovers two critical but underexplored challenges-the fragility of individual temporal sub-networks and the tendency for adversarial vulnerabilities to transfer across time. To overcome these limitations, we propose Robust Temporal self-Ensemble (RTE), a training framework that improves the robustness of each sub-network while reducing the temporal transferability of adversarial perturbations. RTE integrates both objectives into a unified loss and employs a stochastic sampling strategy for efficient optimization. Extensive experiments across multiple benchmarks demonstrate that RTE consistently outperforms existing training methods in robust-accuracy trade-off. Additional analyses reveal that RTE reshapes the internal robustness landscape of SNNs, leading to more resilient and temporally diversified decision boundaries. Our study highlights the importance of temporal structure in adversarial learning and offers a principled foundation for building robust spiking models.