An imperceptible data augmentation based blackbox clean-label backdoor attack on deep neural networks

An imperceptible data augmentation based blackbox clean-label backdoor attack on deep neural networks

Deep neural networks (DNNs) have permeated into many diverse application domains, making them attractive targets of malicious attacks. DNNs are particularly susceptible to data poisoning attacks. Such attacks can be made more venomous and harder to detect by poisoning the training samples without ch...

Full description

Bibliographic Details
Main Authors: Xu, Chaohui, Liu, Wenye, Zheng, Yue, Wang, Si, Chang, Chip Hong
Other Authors: School of Electrical and Electronic Engineering
Format: Journal Article
Language:English
Published: 2024
Subjects:
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Clean-Label Backdoor Attack
Data Augmentation
Data Poisoning
Deep Neural Networks
Edge AI
Online Access:https://hdl.handle.net/10356/173118

Internet

https://hdl.handle.net/10356/173118

Similar Items