Transferability of features for neural networks links to adversarial attacks and defences

The reason for the existence of adversarial samples is still barely understood. Here, we explore the transferability of learned features to Out-of-Distribution (OoD) classes. We do this by assessing neural networks’ capability to encode the existing features, revealing an intriguing connection with...

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Detalles Bibliográficos
Autores principales: Kotyan, Shashank, Matsuki, Moe, Vargas, Danilo Vasconcellos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9045664/
https://www.ncbi.nlm.nih.gov/pubmed/35476838
http://dx.doi.org/10.1371/journal.pone.0266060

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9045664/
https://www.ncbi.nlm.nih.gov/pubmed/35476838
http://dx.doi.org/10.1371/journal.pone.0266060

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