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Exploiting epistemic uncertainty of the deep learning models to generate adversarial samples
Deep neural network (DNN) architectures are considered to be robust to random perturbations. Nevertheless, it was shown that they could be severely vulnerable to slight but carefully crafted perturbations of the input, termed as adversarial samples. In recent years, numerous studies have been conduc...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer US
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8856883/ https://www.ncbi.nlm.nih.gov/pubmed/35221776 http://dx.doi.org/10.1007/s11042-022-12132-7 |