Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/165409
Title: Targeted universal adversarial examples for remote sensing
Authors: Bai, Tao
Wang, Hao
Wen, Bihan
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: Bai, T., Wang, H. & Wen, B. (2022). Targeted universal adversarial examples for remote sensing. Remote Sensing, 14(22), 5833-. https://dx.doi.org/10.3390/rs14225833
Project: RG61/22 
Journal: Remote Sensing 
Abstract: Researchers are focusing on the vulnerabilities of deep learning models for remote sensing; various attack methods have been proposed, including universal adversarial examples. Existing universal adversarial examples, however, are only designed to fool deep learning models rather than target specific goals, i.e., targeted attacks. To this end, we propose two variants of universal adversarial examples called targeted universal adversarial examples and source-targeted universal adversarial examples. Extensive experiments on three popular datasets showed strong attackability of the two targeted adversarial variants. We hope such strong attacks can inspire and motivate research on the defenses against adversarial examples in remote sensing.
URI: https://hdl.handle.net/10356/165409
ISSN: 2072-4292
DOI: 10.3390/rs14225833
Schools: School of Electrical and Electronic Engineering 
Rights: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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