• High Power Laser and Particle Beams
  • Vol. 36, Issue 4, 043019 (2024)
Wenbin Liu1、2, Pingzhi Fan1, Jiahuang Yang3, Yukai Li3, Yuhao Wang3, and Hua Meng3、*
Author Affiliations
  • 1School of Information Science & Technology, Southwest Jiaotong University, Chengdu 611756, China
  • 2The 30th Research Institute of China Electronics Technology Group Corporation, Chengdu 610041, China
  • 3School of Mathematics, Southwest Jiaotong University, Chengdu 611756, China
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    DOI: 10.11884/HPLPB202436.230380 Cite this Article
    Wenbin Liu, Pingzhi Fan, Jiahuang Yang, Yukai Li, Yuhao Wang, Hua Meng. Visual analysis method for RF fingerprint based on important region localization and masking[J]. High Power Laser and Particle Beams, 2024, 36(4): 043019 Copy Citation Text show less
    References

    [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.

    [2] Liu W B, Fan P Z, Wang M H, et al. Optical, acoustic and electromagnetic vulnerability detection for information security[J]. Journal of Physics:Conference Series, 1775, 012001(2021).

    [3] Liu Wenbin, Ding Jianfeng, Kou Yunfeng, . Research on electromagnetic vulnerability of air-gapped network[J]. High Power Laser and Particle Beams, 31, 103215(2019).

    [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.

    [14] Xiao Yao, Wei Xizhang. Specific emitter identification of radar based on one dimensional convolution neural network[J]. Journal of Physics:Conference Series, 1550, 032114(2020).

    [15] Wu Bin, Yuan Shibo, Li Peng, et al. Radar emitter signal recognition based on one-dimensional convolutional neural network with attention mechanism[J]. Sensors, 20, 6350(2020).

    Wenbin Liu, Pingzhi Fan, Jiahuang Yang, Yukai Li, Yuhao Wang, Hua Meng. Visual analysis method for RF fingerprint based on important region localization and masking[J]. High Power Laser and Particle Beams, 2024, 36(4): 043019
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