Kaleidoscope: Physical Backdoor Attacks Against Deep Neural Networks With RGB Filters-Reference-Cited by-同舟云学术

Kaleidoscope: Physical Backdoor Attacks Against Deep Neural Networks With RGB Filters

Published:2023-11 Issue:6 Volume:20 Page:4993-5004
ISSN:1545-5971
Container-title:IEEE Transactions on Dependable and Secure Computing
language:
Short-container-title:IEEE Trans. Dependable and Secure Comput.

Author:

Gong Xueluan¹^ORCID,Wang Ziyao²,Chen Yanjiao³^ORCID,Xue Meng¹,Wang Qian²^ORCID,Shen Chao⁴^ORCID

Affiliation:

1. School of Computer Science, Wuhan University, Wuhan, Hubei, China

2. School of Cyber Science and Engineering, Wuhan University, Wuhan, Hubei, China

3. College of Electrical Engineering, Zhejiang University, Hangzhou, Zhejiang, China

4. School of Cyber Science and Engineering, MOE Key Laboratory for Intelligent Networks and Network Security, Xi’an Jiaotong University, Xi’an, Shaanxi, China

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/8858/10314839/10024758.pdf?arnumber=10024758

Reference45 articles.

1. Backdoor Attacks and Defenses for Deep Neural Networks in Outsourced Cloud Environments

2. Model inversion attacks against collaborative inference

3. Backdoor embedding in convolutional neural network models via invisible perturbation;liao,2018

4. Membership Inference Attacks Against Machine Learning Models

5. TrojanZoo: Everything you ever wanted to know about neural backdoors (but were afraid to ask);pang,2020

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