Randomized Purifier Based on Low Adversarial Transferability for Adversarial Defense

Deep neural networks are generally very vulnerable to adversarial attacks. In order to defend against adversarial attacks in classifiers, Adversarial Purification (AP) was developed to neutralize adversarial perturbations using a generative model at the input stage. AP has an advantage in that it ca...

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Bibliographic Details
Published in:IEEE Access
Main Authors: Sangjin Park, Yoojin Jung, Byung Cheol Song
Format: Article
Language:English
Published: IEEE 2024-01-01
Subjects:
Adversarial attack
adversarial defense
computer vision
deep learning
image classification
security
Online Access:https://ieeexplore.ieee.org/document/10630788/

Internet

https://ieeexplore.ieee.org/document/10630788/

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