The following publications are possibly variants of this publication:
- Threats of Adversarial Attacks in DNN-Based Modulation RecognitionYun Lin 0005, Haojun Zhao, Ya Tu, Shiwen Mao, Zheng Dou. infocom 2020: 2469-2478 [doi]
- Evaluating and Improving Adversarial Attacks on DNN-Based Modulation RecognitionHaojun Zhao, Yun Lin 0005, Song Gao, Shui Yu. globecom 2020: 1-5 [doi]
- Defending AI-Based Automatic Modulation Recognition Models Against Adversarial AttacksHaolin Tang, Ferhat Özgür Çatak, Murat Kuzlu, Evren Çatak, Yanxiao Zhao. access, 11:76629-76637, 2023. [doi]
- An Illumination Modulation-Based Adversarial Attack Against Automated Face Recognition SystemZhaojie Chen, Puxi Lin, Zoe Lin Jiang, Zhanhang Wei, Sichen Yuan, Junbin Fang. cisc 2021: 53-69 [doi]
- Adversarial Attack for Modulation Recognition Based on Deep Neural NetworksJunyi Zhang, Chun Li, Jiayong Zhao, Wei Tang, Shichao Guan. smartiot 2023: 310-314 [doi]
- Position-Invariant Adversarial Attacks on Neural Modulation RecognitionZhen Yu, Yifeng Xiong, Kun He 0001, Shao Huang, Yaodong Zhao, Jie Gu. icassp 2022: 3483-3487 [doi]
- Adversarial Attacks in Modulation Recognition With Convolutional Neural NetworksYun Lin 0005, Haojun Zhao, Xuefei Ma, Ya Tu, Meiyu Wang. tr, 70(1):389-401, 2021. [doi]