Research Paper#Spiking Neural Networks, Adversarial Robustness, Machine Learning🔬 ResearchAnalyzed: Jan 3, 2026 16:26
Reliable Adversarial Robustness Evaluation for Spiking Neural Networks
Published:Dec 27, 2025 08:43
•1 min read
•ArXiv
Analysis
This paper addresses the challenge of evaluating the adversarial robustness of Spiking Neural Networks (SNNs). The discontinuous nature of SNNs makes gradient-based adversarial attacks unreliable. The authors propose a new framework with an Adaptive Sharpness Surrogate Gradient (ASSG) and a Stable Adaptive Projected Gradient Descent (SA-PGD) attack to improve the accuracy and stability of adversarial robustness evaluation. The findings suggest that current SNN robustness is overestimated, highlighting the need for better training methods.
Key Takeaways
- •Proposes a more reliable framework for evaluating SNN adversarial robustness.
- •Introduces Adaptive Sharpness Surrogate Gradient (ASSG) to improve gradient accuracy.
- •Designs Stable Adaptive Projected Gradient Descent (SA-PGD) for faster and more stable convergence.
- •Demonstrates that current SNN robustness is overestimated.
- •Highlights the need for more dependable adversarial training methods.
Reference
“The experimental results further reveal that the robustness of current SNNs has been significantly overestimated and highlighting the need for more dependable adversarial training methods.”