Perception-Driven Imperceptible Adversarial Attack Against Decision-Based Black-Box Models-Reference-Cited by-同舟云学术

Perception-Driven Imperceptible Adversarial Attack Against Decision-Based Black-Box Models

Published:2024 Issue: Volume:19 Page:3164-3177
ISSN:1556-6013
Container-title:IEEE Transactions on Information Forensics and Security
language:
Short-container-title:IEEE Trans.Inform.Forensic Secur.

Author:

Zhang Shenyi¹^ORCID,Zheng Baolin²,Jiang Peipei¹^ORCID,Zhao Lingchen¹^ORCID,Shen Chao³^ORCID,Wang Qian¹^ORCID

Affiliation:

1. Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, China

2. Alibaba Group, Beijing, China

3. Ministry of Education (MOE) Key Laboratory for Intelligent Networks and Network Security, School of Cyber Science and Engineering, Xi’an Jiaotong University, Xi’an, China

Funder

National Key Research and Development Program of China

NSFC

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/10206/10319981/10415445.pdf?arnumber=10415445

Reference58 articles.

1. Explaining and harnessing adversarial examples;Goodfellow

2. The Limitations of Deep Learning in Adversarial Settings

3. Decision-based adversarial attacks: Reliable attacks against black-box machine learning models;Brendel

4. HopSkipJumpAttack: A Query-Efficient Decision-Based Attack

5. Dirty road can attack: Security of deep learning based automated lane centering under physical-world attack;Sato

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Generative Imperceptible Attack With Feature Learning Bias Reduction and Multi-Scale Variance Regularization;IEEE Transactions on Information Forensics and Security;2024