KAN-Robust-Bench: A Benchmark for Evaluating the Robustness of Kolmogorov-Arnold Networks

2026-08-21

AI Summary

研究通过随机平滑和区间边界传播方法，评估了Kolmogorov-Arnold网络模型在对抗逃避攻击下的鲁棒性，并探索了最佳防御策略。

Abstract

While machine learning models have demonstrated strong performance in many domains, these models have shown profound vulnerabilities when they are exposed to adversarial threats. While adversarial attacks fall into various categories, the most prominent category in research studies is evasion. In evasion attacks, the adversary generates perturbed versions of samples, which might not be observable by human eyes. These samples generally fool the machine learning models with high confidence. This phenomenon poses a significant security violation against machine learning models. In this paper, we investigate the certified and empirical robustness of various Kolmogorov-Arnold network architectures against strong evasion attacks. At first, we provide the mathematical foundations for randomized smoothing and interval bound propagation, and report the $\ell_2$-certified robustness of the models under randomized smoothing. After that, we systematically evaluate the robustness of various defended and undefended KAN models under FGSM, PGD, and C&W attacks in order to find out the optimal defense strategies and architectures.

Problem

• adversarial threats
• evasion attacks
• machine learning models
• robustness

Innovation

• Kolmogorov-Arnold Networks
• Adversarial Attacks
• Randomized Smoothing
• Interval Bound Propagation
• Certified Robustness