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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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11078278/ |
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