Adversarial Attacks Against Machine Learning-Based Resource Provisioning Systems-Reference-Cited by-同舟云学术

Adversarial Attacks Against Machine Learning-Based Resource Provisioning Systems

Published:2023-09 Issue:5 Volume:43 Page:35-44
ISSN:0272-1732
Container-title:IEEE Micro
language:
Short-container-title:IEEE Micro

Author:

Nazari Najmeh¹^ORCID,Makrani Hosein Mohammadi²^ORCID,Fang Chongzhou¹^ORCID,Omidi Behnam³^ORCID,Rafatirad Setareh¹^ORCID,Sayadi Hossein⁴^ORCID,Khasawneh Khaled N.³^ORCID,Homayoun Houman¹^ORCID

Affiliation:

1. University of California Davis, Davis, CA, USA

2. Apple, Inc., Cupertino, CA, USA

3. George Mason University, Fairfax, VA, USA

4. California State University Long Beach, Long Beach, CA, USA

Funder

National Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Hardware and Architecture,Software

Link

https://ieeexplore.ieee.org/ielam/40/10248411/10155235-aam.pdf

Reference16 articles.

1. Defense Against Universal Adversarial Perturbations

2. DeepCloak: Masking deep neural network models for robustness against adversarial samples;gao,2017

3. Feature squeezing: Detecting adversarial examples in deep neural networks;xu,2017

4. Generative adversarial trainer: Defense to adversarial perturbations with gan;lee,2017

5. Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks

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