• Journal of Terahertz Science and Electronic Information Technology
  • Vol. 20, Issue 8, 836 (2022)
ZHAO Haojun1、2、*, LIN Yun1、2, BAO Zhida1、2, SHI Jibo1、2, and GE Bin3
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • 3[in Chinese]
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    DOI: 10.11805/tkyda2020692 Cite this Article
    ZHAO Haojun, LIN Yun, BAO Zhida, SHI Jibo, GE Bin. Targeted adversarial attack in modulation recognition[J]. Journal of Terahertz Science and Electronic Information Technology , 2022, 20(8): 836 Copy Citation Text show less
    References

    [1] O'SHEA T J, ROY T, CLANCY T C. Over-the-air deep learning based radio signal classification[J]. IEEE Journal of Selected Topics in Signal Processing, 2018,12(1):168-179.

    [2] O'SHEA T, HOYDIS J. An introduction to deep learning for the physical layer[J]. IEEE Transactions on Cognitive Communications and Networking, 2017,3(4):563-575.

    [3] LIN Y,TU Y,DOU Z,et al. The application of deep learning in communication signal modulation recognition[C]// 2017 IEEE/CIC International Conference on Communications in China(ICCC). Qingdao,China:IEEE, 2017:1-5.

    [5] SUN H,CHEN X,SHI Q,et al. Learning to optimize:training deep neural networks for wireless resource management[C]// 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications(SPAWC). Sapporo,Japan:IEEE, 2017:1-6.

    [7] TU Y, LIN Y, WANG J, et al. Semi-supervised learning with generative adversarial networks on digital signal modulation classification[J]. Computers, Materials & Continua, 2018,55(2):243-254.

    [9] SZEGEDY C,ZAREMBA W,SUTSKEVR I,et al. Intriguing properties of neural networks[C]// 2nd International Conference on Learning Representations. Banff,Canada:[s.n.], 2012:1-10.

    [10] LIN Y,ZHAO H,TU Y,et al. Threats of adversarial attacks in DNN-based modulation recognition[C]// 2020 IEEE Conference on Computer Communications. Toronto,ON,Canada:IEEE, 2020:2469-2478.

    [11] MADRY A, MAKELOV A, SCHMIDT L, et al. Towards deep learning models resistant to adversarial attacks[C]// The 6th International Conference on Learning Representations. Vancouver,Canada:[s.n.], 2016:1-23.

    [12] KURAKIN A,GOODFELLOW I,BENGIO S. Adversarial examples in the physical world[C]// The 6th International Conference on Learning Representations. Vancouver,Canada:[s.n.], 2016:128-141.

    [13] GOODFELLOW I J, SHLENS J, SZEGEDY C. Explaining and harnessing adversarial examples[C]// The 5th International Conference on Learning Representations. San Diego,CA,USA:2015:1-11.

    [14] YUAN X, HE P, ZHU Q, et al. Adversarial examples: attacks and defenses for deep learning[J]. IEEE Transactions on Neural Networks and Learning Systems, 2019,30(9):2805-2824.

    [15] O'SHEA T, WEST N. Radio machine learning dataset generation with GNU radio[J]. Proceedings for the 6th GNU Radio Conference, 2016,1(1):1-6.

    [16] DeepSig. Deepsig dataset:radioml 2016[EB/OL]. [2020-12-10]. https://www.deepsig.io/datasets.

    ZHAO Haojun, LIN Yun, BAO Zhida, SHI Jibo, GE Bin. Targeted adversarial attack in modulation recognition[J]. Journal of Terahertz Science and Electronic Information Technology , 2022, 20(8): 836
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