Hao Wang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Hao Wang has a total of 2 co-authors. Here are the top co-authors by number of shared publications:
- Bihan Wen
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
1 shared publication - Tao Bai
School of Electrical and Electronic Engineering, Nanyang Technological University, Jurong West, Singapore
1 shared publication
Latest Publications
- Open Access
- 17 November 2022
- Published by MDPI AG in Remote Sensing
- Vol. 14 (22) , 5833
Targeted Universal Adversarial Examples for Remote Sensing
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.
- 17 November 2022
- Published by MDPI AG in Remote Sensing
- Vol. 14 (22) , 5833
Open AccessTargeted Universal Adversarial Examples for Remote Sensing
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.