Cargando…
DualFlow: Generating imperceptible adversarial examples by flow field and normalize flow-based model
Recent adversarial attack research reveals the vulnerability of learning-based deep learning models (DNN) against well-designed perturbations. However, most existing attack methods have inherent limitations in image quality as they rely on a relatively loose noise budget, i.e., limit the perturbatio...
Autores principales: | Liu, Renyang, Jin, Xin, Hu, Dongting, Zhang, Jinhong, Wang, Yuanyu, Zhang, Jin, Zhou, Wei |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9947527/ https://www.ncbi.nlm.nih.gov/pubmed/36845066 http://dx.doi.org/10.3389/fnbot.2023.1129720 |
Ejemplares similares
-
Local imperceptible adversarial attacks against human pose estimation networks
por: Liu, Fuchang, et al.
Publicado: (2023) -
Dual-flow network with attention for autonomous driving
por: Yang, Lei, et al.
Publicado: (2023) -
Synthetic flow-based cryptomining attack generation through Generative Adversarial Networks
por: Mozo, Alberto, et al.
Publicado: (2022) -
Generative adversarial networks to infer velocity components in rotating turbulent flows
por: Li, Tianyi, et al.
Publicado: (2023) -
Imperceptible magnetoelectronics
por: Melzer, Michael, et al.
Publicado: (2015)