Forming Adversarial Example Attacks Against Deep Neural Networks With Reinforcement Learning

被引：0

作者：

Akers, Matthew ^{[1
]}

Barton, Armon ^{[2
]}

机构：

[1] US Second Fleet, Hampton Rd, Norfolk, VA 23455 USA

[2] Dept Comp Sci Naval Postgrad Sch, Dept Comp Sci, Monterey, CA 93943 USA

来源：

COMPUTER | 2024年 / 57卷 / 01期

关键词：

Deep learning; Perturbation methods; Reinforcement learning; Artificial neural networks; GAME; GO;

D O I：

10.1109/MC.2023.3324751

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

We propose a novel reinforcement learning-based adversarial example attack, Adversarial Reinforcement Learning Agent, designed to learn imperceptible perturbation that causes misclassification when added to the input of a deep learning classifier.

引用

页码：88 / 99

页数：12

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