Imperceptible misclassification attack on deep learning accelerator by glitch injection
The convergence of edge computing and deep learning empowers endpoint hardwares or edge devices to perform inferences locally with the help of deep neural network (DNN) accelerator. This trend of edge intelligence invites new attack vectors, which are methodologically different from the well-known s...
Main Authors: | Liu, Wenye, Chang, Chip-Hong, Zhang, Fan, Lou, Xiaoxuan |
---|---|
Other Authors: | School of Electrical and Electronic Engineering |
Format: | Conference Paper |
Language: | English |
Published: |
2021
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/145856 |
Similar Items
-
Stealthy and robust glitch injection attack on deep learning accelerator for target with variational viewpoint
by: Liu, Wenye, et al.
Published: (2021) -
Vulnerability analysis on noise-injection based hardware attack on deep neural networks
by: Liu, Wenye, et al.
Published: (2020) -
Fault-injection based attacks and countermeasure on deep neural network accelerators
by: Liu, Wenye
Published: (2021) -
An imperceptible data augmentation based blackbox clean-label backdoor attack on deep neural networks
by: Xu, Chaohui, et al.
Published: (2024) -
A practical man-in-the-middle attack on deep learning edge device by sparse light strip injection into camera data lane
by: Liu, Wenye, et al.
Published: (2023)