DACO-BD: Data Augmentation Combinatorial Optimization-Based Backdoor Defense in Deep Neural Networks for SAR Image Classification-Reference-Cited by-同舟云学术

DACO-BD: Data Augmentation Combinatorial Optimization-Based Backdoor Defense in Deep Neural Networks for SAR Image Classification

Published:2024 Issue: Volume:73 Page:1-13
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Zeng Guo-Qiang¹^ORCID,Wei Hai-Nan²^ORCID,Lu Kang-Di³^ORCID,Geng Guang-Gang²^ORCID,Weng Jian²^ORCID

Affiliation:

1. National-Local Joint Engineering Research Center of Digitalized Electrical Design Technology, Wenzhou University, Wenzhou, China

2. College of Cyber Security and the National Joint Engineering Research Center of Network Security Detection and Protection Technology, Jinan University, Guangzhou, China

3. College of Information Science and Technology, Donghua University, Shanghai, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Guangdong Province

Guangdong Key Laboratory of Data Security and Privacy Preserving

National Joint Engineering Research Center of Network Security Detection and Protection Technology

Fundamental Research Funds for the Central Universities

Pearl River Talents Plan

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/19/10367905/10616102.pdf?arnumber=10616102

Reference47 articles.

1. Backdoor Learning: A Survey

2. A tutorial on synthetic aperture radar

3. A Fast Algorithm for High Accuracy Airborne SAR Geolocation Based on Local Linear Approximation

4. Active Multiview Fusion Framework for SAR Automatic Target Recognition

5. A Stretched Deramping Radar Technique for High-Resolution SAR Processing in Ka-Band Using the Extended Integration Time