EITGAN: A Transformation-based Network for recovering adversarial examples

EITGAN: A Transformation-based Network for recovering adversarial examples

Adversarial examples have been shown to easily mislead neural networks, and many strategies have been proposed to defend them. To address the problem that most transformation-based defense strategies will degrade the accuracy of clean images, we proposed an Enhanced Image Transformation Generative A...

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Bibliographic Details
Main Authors:	Junjie Zhao, Junfeng Wu, James Msughter Adeke, Guangjie Liu, Yuewei Dai
Format:	Article
Language:	English
Published:	AIMS Press 2023-10-01
Series:	Electronic Research Archive
Subjects:	enhanced sample generative adversarial network adversarial defense deep learning adversarial example image processing
Online Access:	https://www.aimspress.com/article/doi/10.3934/era.2023335?viewType=HTML

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