BriefGPT.xyz
Mar, 2020
热度与模糊:对抗性示例的有效快速防御
Heat and Blur: An Effective and Fast Defense Against Adversarial Examples
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Haya Brama, Tal Grinshpoun
TL;DR
结合特征可视化和输入修改的简单防御方法,能够应用于各种预训练模型,可对付对神经网络造成的敌对攻击。本文在ImageNet数据集上以VGG19为例,通过新的评估指标验证了该防御方法的有效性。
Abstract
The growing incorporation of artificial
neural networks
(NNs) into many fields, and especially into life-critical systems, is restrained by their vulnerability to
adversarial examples
(AEs). Some existing defense
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