English(EN) Dual Randomized Smoothing: Beyond Global Noise Variance

双重随机平滑增强神经网络鲁棒性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-16 04:00

研究人员推出了一种名为双重随机平滑（Dual RS）的新型框架，旨在增强神经网络对抗对抗性扰动的鲁棒性。与使用单一全局噪声方差的传统随机平滑不同，Dual RS 采用依赖于输入的噪声方差。这种方法能够在较小和较大的扰动半径下都获得更好的性能，克服了现有方法的一个关键限制。在 CIFAR-10 和 ImageNet 数据集上的实验表明，Dual RS 的性能显著优于先前的方法，在计算开销仅略微增加的情况下提供了改进的准确性-鲁棒性权衡。 AI

影响引入了一种提高神经网络对抗对抗性攻击鲁棒性的新颖技术，有望带来更安全的 AI 系统。

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Chenhao Sun, Yuhao Mao, Martin Vechev · 2026-09-16 04:00

双重随机平滑：超越全局噪声方差

arXiv:2512.01782v4 Announce Type: replace-cross Abstract: Randomized Smoothing (RS) is a prominent technique for certifying the robustness of neural networks against adversarial perturbations. With RS, achieving high accuracy at small radii requires a small noise variance, while …

本文如何被排名

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双重随机平滑：超越全局噪声方差

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