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

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

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