Adversarial robustness in deep neural networks based on variable attributes of the stochastic ensemble model.

Qin, Ruoxi; Wang, Linyuan; Du, Xuehui; Xie, Pengfei; Chen, Xingyuan; Yan, Bin

Qin, Ruoxi; Wang, Linyuan; Du, Xuehui; Xie, Pengfei; Chen, Xingyuan; Yan, Bin.

Afiliación

Qin R; Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, Zhengzhou, Henan, China.
Wang L; PLA Strategy Support Force Information Engineering University, Zhengzhou, Henan, China.
Du X; Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, Zhengzhou, Henan, China.
Xie P; Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, Zhengzhou, Henan, China.
Chen X; PLA Strategy Support Force Information Engineering University, Zhengzhou, Henan, China.
Yan B; Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, Zhengzhou, Henan, China.

Front Neurorobot ; 17: 1205370, 2023.

Article en En | MEDLINE | ID: mdl-37614968

Palabras clave

adversarial robustness; cyberspace security; deep neural network; random smoothing; stochastic ensemble

Texto completo

Añadir a Mi BVS

Imprimir

XML

PubMed Links

Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Front Neurorobot Año: 2023 Tipo del documento: Article País de afiliación: China

Texto completo

Añadir a Mi BVS

Imprimir

XML

PubMed Links

Buscar en Google