APMSA: Adversarial Perturbation Against Model Stealing Attacks-Reference-Cited by-同舟云学术

APMSA: Adversarial Perturbation Against Model Stealing Attacks

Published:2023 Issue: Volume:18 Page:1667-1679
ISSN:1556-6013
Container-title:IEEE Transactions on Information Forensics and Security
language:
Short-container-title:IEEE Trans.Inform.Forensic Secur.

Author:

Zhang Jiliang¹^ORCID,Peng Shuang²,Gao Yansong³^ORCID,Zhang Zhi⁴^ORCID,Hong Qinghui⁵^ORCID

Affiliation:

1. Changsha Semiconductor Technology and Application Innovation Research Institute, College of Semiconductors (College of Integrated Circuits), Hunan University, Changsha, China

2. College of Semiconductors (College of Integrated Circuits), Hunan University, Changsha, China

3. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

4. Department of Computer Science and Software Engineering, The University of Western Australia, Perth, WA, Australia

5. College of Computer Science and Electronic Engineering, Hunan University, Changsha, China

Funder

National Natural Science Foundation of China

Science and Technology Innovation Program of Hunan Province

Natural Science Foundation of Fujian Province

Natural Science Foundation of Jiangsu Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Safety, Risk, Reliability and Quality

Link

http://xplorestaging.ieee.org/ielx7/10206/9970396/10049136.pdf?arnumber=10049136

Reference27 articles.

1. ATMPA

2. Practical Black-Box Attacks against Machine Learning

3. CloudLeak: Large-Scale Deep Learning Models Stealing Through Adversarial Examples

4. Model Stealing Defense with Hybrid Fuzzy Models: Work-in-Progress

5. Prediction poisoning: Towards defenses against DNN model stealing attacks;orekondy;arXiv 1906 10908,2019

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