A Universal Detection Method for Adversarial Examples and Fake Images

Deep-learning technologies have shown impressive performance on many tasks in recent years. However, there are multiple serious security risks when using deep-learning technologies. For examples, state-of-the-art deep-learning technologies are vulnerable to adversarial examples that make the model’s...

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Detalles Bibliográficos
Autores principales: Lai, Jiewei, Huo, Yantong, Hou, Ruitao, Wang, Xianmin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9099751/
https://www.ncbi.nlm.nih.gov/pubmed/35591134
http://dx.doi.org/10.3390/s22093445

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9099751/
https://www.ncbi.nlm.nih.gov/pubmed/35591134
http://dx.doi.org/10.3390/s22093445

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