[1] AlShawabka A, Restuccia F, D’o S, et al. Exposing the fingerprint: dissecting the impact of the wireless channel on radio fingerprinting[C]Proceedings of the IEEE INFOCOM 2020 IEEE Conference on Computer Communications. 2020: 646655.
[4] José A. Gutiérrez del Arroyo Pérez. Learning robust radio frequency fingerprints using deep convolutional neural wks[D]. USA: Air Fce Institute of Technology, 2022.
[5] Yang Zhou, Liu Ninghao, Hu Xiaben, et al. Tutial on deep learning interpretation: a data perspective[C]Proceedings of the 31st ACM International Conference on Infmation & Knowledge Management. 2022: 51565159.
[6] Srivastava G, Jhaveri R H, Bhattaya S, et al. XAI f cybersecurity: state of the art, challenges, open issues future directions[DBOL]. arXiv preprint arXiv: 2206.03585, 2022.
[7] Li Hui. Research on visual interpretable method of convolutional neural wks based on class activation mapping[D]. Changchun: Jilin University, 2023
[8] Selvaraju R R, Cogswell M, Das A, et al. GradCAM: visual explanations from deep wks via gradientbased localization[C]Proceedings of 2017 IEEE International Conference on Computer Vision. 2017: 618626.
[9] Kim J, Oh J, Heo T Y. Acoustic scene classification and visualization of beehive sounds using machine learning algorithms and grad-CAM[J]. Mathematical Problems in Engineering, 2021, 5594498(2021).
[10] Liang Xianming, Ni Fan, Chen Wenjie, et al. Interpretability of modulation recognition wk based on timefrequency gradCAM[JOL]. Journal of Southwest Jiaotong University, 2022. https:kns.cnki.kcmsdetail51.1277.u.20220608.1636.008.html.
[11] Ni Fan. Research on communication signal modulation recognition algithm based on interpretable deep learning[D]. Chengdu: Southwest Jiaotong University, 2022
[12] Liu Wenbin, Fan Pingzhi, Li Yukai, et al. An interpretable testing architecture f specific emitter identification[J]. Journal of Terahertz Science Electronic Infmation Technology, 2023, 21(6): 734744
[13] He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al. Deep residual learning f image recognition[C]Proceedings of 2016 IEEE Conference on Computer Vision Pattern Recognition. 2016: 770778.