English(EN) On the Existence of Consistent Adversarial Attacks in High-Dimensional Linear Classification

新指标量化模型对对抗性攻击的脆弱性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-02 04:00

研究人员开发了一种新指标，用于区分高维线性分类中的对抗性攻击和标准错误分类。该指标量化了模型对保留标签的扰动的脆弱性。研究的理论发现表明，模型过度参数化程度的增加与对这些对抗性攻击的易感性增加相关，从而揭示了模型敏感性背后的机制。 AI

影响为模型对对抗性攻击的脆弱性提供了理论见解，可能指导未来在人工智能安全和鲁棒性方面的研究。

排序理由关于机器学习理论方面的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv stat.ML TIER_1 English(EN) · Matteo Vilucchio, Lenka Zdeborov\'a, Bruno Loureiro · 2026-09-02 04:00

高维线性分类中一致对抗性攻击的存在性

arXiv:2506.12454v2 Announce Type: replace Abstract: What fundamentally distinguishes an adversarial attack from a misclassification due to limited model expressivity or finite data? In this work, we investigate this question in the setting of high-dimensional binary classificatio…