D3R-Net: Denoising Diffusion-Based Defense Restore Network for Adversarial Defense in Remote Sensing Scene Classification-Reference-Cited by-同舟云学术

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D3R-Net: Denoising Diffusion-Based Defense Restore Network for Adversarial Defense in Remote Sensing Scene Classification

Published:2024 Issue: Volume:62 Page:1-14
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Liu Xuehu¹^ORCID,Feng Zhixi¹^ORCID,Ma Yue¹,Yang Shuyuan¹^ORCID,Chang Zhihao¹,Jiao Licheng¹^ORCID

Affiliation:

1. School of Artificial Intelligence, Xidian University, Xi’an, China

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Science and Technology Program of Guangzhou, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/36/10354519/10553318.pdf?arnumber=10553318

Reference53 articles.

1. Assessing the Threat of Adversarial Examples on Deep Neural Networks for Remote Sensing Scene Classification: Attacks and Defenses

2. Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities

3. Object detection in remote sensing imagery using a discriminatively trained mixture model

4. Remote Sensing Image Retrieval With Global Morphological Texture Descriptors

5. Very deep convolutional networks for large-scale image recognition;Simonyan;arXiv:1409.1556,2014

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