Vulnerability analysis on noise-injection based hardware attack on deep neural networks

Vulnerability analysis on noise-injection based hardware attack on deep neural networks

Despite superior accuracy on most vision recognition tasks, deep neural networks are susceptible to adversarial examples. Recent studies show that adding carefully crafted small perturbations on input layer can mislead a classifier into arbitrary categories. However, most adversarial attack algorith...

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Bibliographic Details
Main Authors: Liu, Wenye, Wang, Si, Chang, Chip-Hong
Other Authors: School of Electrical and Electronic Engineering
Format: Conference Paper
Language:English
Published: 2020
Subjects:
Engineering::Electrical and electronic engineering
Deep Neural Networks
Hardware Attacks
Online Access:https://hdl.handle.net/10356/136863

Internet

https://hdl.handle.net/10356/136863

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