An empirical study on correlation between coverage and robustness for deep neural networks
Deep neural networks (DNN) are increasingly applied in safety-critical systems, e.g., for face recognition, autonomous car control and malware detection. It is also shown that DNNs are subject to attacks such as adversarial perturbation and thus must be properly tested. Many coverage criteria for DN...
Saved in:
Main Authors: | DONG, Yizhen, ZHANG, Peixin, WANG, Jingyi, LIU, Shuang, SUN, Jun, HAO, Jianye, WANG, Xinyu, WANG, Li, DONG, Jinsong, DAI, Ting |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2020
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/5942 https://ink.library.smu.edu.sg/context/sis_research/article/6945/viewcontent/Emp_coverage_robustness_DNN_2020_av.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
MobiDroid: A performance-sensitive malware detection system on mobile platform
by: FENG, Ruitao, et al.
Published: (2019) -
The Computational and Performance Aspects of Masked Face Detection and Recognition
by: Kachasak Intim
Published: (2023) -
Noise-Robust Speech Recognition Using Deep Neural Network
by: LI BO
Published: (2014) -
Using a Markov network to recognize people in consumer images
by: Gallagher A.C., et al.
Published: (2018) -
Face recognition with accessories using CNN
by: Muhammad Shafiq B Ninaba
Published: (2024)