新RRISE方法大幅降低AI鲁棒性认证成本

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-03 04:00

研究人员开发了RRISE，一个用于鲁棒半径推断的新框架，它显著加快了认证$\ell_2$分类鲁棒性的过程。通过训练一个学习到的代理模型，RRISE用一次前向传播取代了数千次蒙特卡洛采样步骤，使得认证鲁棒性在实时系统中变得实用。该方法在认证精度上与传统的随机平滑法相当，同时大幅降低了部署期间的计算成本。 AI

影响通过降低计算开销，实现了认证AI鲁棒性的实用化和实时应用。

排序理由该集群包含一篇详细介绍改进AI鲁棒性新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 Română(RO) · Jong-Ik Park, Shreyas Chaudhari, Carlee Joe-Wong, Jos\'e M. F. Moura · 2026-06-03 04:00

RRISE：一种替代估计器的鲁棒半径推断

arXiv:2606.02876v1 Announce Type: new Abstract: Randomized smoothing (RS) uses a smoothed classifier to provide architecture-agnostic certificates of $\ell_2$ classification robustness, but its dependence on per-input Monte Carlo (MC) sampling undermines its use in real-time syst…

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报道来源 [1]

RRISE：一种替代估计器的鲁棒半径推断

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