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Improving the Transferability of Adversarial Examples With a Noise Data Enhancement Framework and Random Erasing
Deep neural networks (DNNs) are proven vulnerable to attack against adversarial examples. Black-box transfer attacks pose a massive threat to AI applications without accessing target models. At present, the most effective black-box attack methods mainly adopt data enhancement methods, such as input...
| Autores principales: | , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Frontiers Media S.A.
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8696674/ https://www.ncbi.nlm.nih.gov/pubmed/34955802 http://dx.doi.org/10.3389/fnbot.2021.784053 |