A Pruning Method Combined with Resilient Training to Improve the Adversarial Robustness of Automatic Modulation Classification Models
Author:
Funder
National Defense Key Laboratory Fund
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11036-024-02333-9.pdf
Reference46 articles.
1. Lin, Y., Zhao, H., Tu, Y et al (2020) Threats of adversarial attacks in dnn-based modulation recognition. In: IEEE INFOCOM 2020-IEEE Conference on Computer Communications, IEEE pp. 2469–2478
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3. Xuan Q, Zhou J, Qiu K et al (2022) Avgnet: Adaptive visibility graph neural network and its application in modulation classification. IEEE Trans Netw Sci Eng 9(3):1516–1526. https://doi.org/10.1109/TNSE.2022.3146836
4. Qi P, Zhou X, Zheng S et al (2021) Automatic modulation classification based on deep residual networks with multimodal information. IEEE Trans Cogn Commun Netw 7(1):21–33. https://doi.org/10.1109/TCCN.2020.3023145
5. Chen Z, Wang Z, Xu D et al (2024) Learn to defend: Adversarial multi-distillation for automatic modulation recognition models. IEEE Trans Inf Forensic Secur 1–1. https://doi.org/10.1109/TIFS.2024.3361172
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