Adversarial Domain Generalization Defense for Automatic Modulation Classification
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2023 IEEE/CIC International Conference on Communications in China (ICCC), 2023, 00, pp. 1-6
- Issue Date:
- 2023-09-05
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Adversarial_Domain_Generalization_Defense_for_Automatic_Modulation_Classification.pdf | Published version | 4.27 MB |
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Automatic modulation classification AMC technology plays a vital role in the sixth generation mobile system 6G However deep learning DL based AMC models possess unexpected vulnerability against adversarial examples which seriously affects their applications in 6G To this end we propose an Adversarial Domain Generalization Defense ADGD algorithm to improve the adversarial robustness of DL based AMC models Firstly we sequentially pre train two classifiers that classify the original signals and the adversarial examples respectively Secondly we extract task relevant features of the original signals and adversarial examples and align them Finally we use adversarial training to enhance the adversarial robustness of the models The comparative experiments with various defense algorithms under white box and black box conditions of various attack algorithms demonstrate the outstanding defense performance of the ADGD algorithm The proposed solution is of great significance to promote the application of AMC technology in 6G
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