arXiv cs.AI· Rahul Kumar Yadav, Tunir Ghosh, Sudhendu Ahir, Parthe Pandit, Arjun Bhagoji·· 4 小时前AI 评分33
核函数机对抗鲁棒性：一项全面分析
Adversarial Robustness of Kernel Machines: A Comprehensive Analysis
AI 导读
一篇 arXiv 论文对核函数机的对抗鲁棒性进行了全面实证分析，通过白盒与黑盒规避攻击评估二元及多分类任务中核机器、随机特征分类器和递归特征机的原生鲁棒性，并提出了核机器近似对抗训练算法。图像与表格数据集上的实验表明，核函数机天然比神经网络更鲁棒，面对对抗扰动时性能下降更平缓。
正文 · 原文
Abstract:Kernel machines are an important class of models in machine learning. However, the question of how robust modern kernel machines are to adversarial examples has not been studied comprehensively. In this paper, we provide a thorough empirical investigation of the adversarial robustness of kernel-based models for both binary and multiclass classification tasks, via white-box and black-box evasion attacks. We evaluate the native robustness of kernel machines, random feature classifiers, and the relatively newer Recursive Feature Machines. In addition, we provide an algorithm for approximate adversarial training of kernel machines. Our experiments on classification tasks on image and tabular datasets strongly indicate that kernel machines are natively more robust than neural networks, i.e., their performance degrades more gracefully against adversarial perturbations.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.07474 [cs.LG] |
| (or arXiv:2611.07474v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2611.07474 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Rahul Yadav [view email]
[v1]
Thu, 5 Nov 2026 05:33:09 UTC (6,959 KB)
来源：arXiv cs.AI · arxiv.org