The strength of Nesterov's accelerated gradient in boosting transferability of stealthy adversarial attacks.
Deep neural networks have been shown to be highly vulnerable to adversarial examples-inputs crafted to mislead models by adding subtle, human-imperceptible perturbations. Transferability and stealthiness are two crucial metrics for evaluating adversarial attacks. However, these goals often conflict:...
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| Main Authors: | , |
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| Format: | Artigo |
| Language: | Inglês |
| Published: |
Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0337463 |
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