Perceptual Carlini-Wagner Attack: A Robust and Imperceptible Adversarial Attack Using LPIPS

Adversarial attacks on deep neural networks (DNNs) present significant challenges by exploiting model vulnerabilities using perturbations that are often imperceptible to human observers. Traditional approaches typically constrain perturbations using p-norms, which do not effectively capture human pe...

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Bibliographic Details

Published in:	IEEE Access
Main Authors:	Liming Fan, Anis Salwa Mohd Khairuddin, Haichuan Liu, Khairunnisa Binti Hasikin
Format:	Article
Language:	English
Published:	IEEE 2025-01-01
Subjects:	Adversarial attacks perceptual similarity LPIPS deep neural networks robustness transferability
Online Access:	https://ieeexplore.ieee.org/document/11078278/