面向自动调制分类的域自适应对抗防御方法OA

Deep learning-based automatic modulation classification(AMC)models are vulnerable to adversarial example attacks,posing severe adversarial security threats in practical scenarios characterized by dynamic channel conditions and limited availability of signal labels.To address this issue,a multi-domain distribution alignment based on domain-adaptive adversarial defense method was proposed.Firstly,a phase rotation data augmentation strategy was used to en-rich the discriminative and domain-invariant features learned by the model.Secondly,a dual-discriminator architecture was constructed to reduce the feature distribution discrepancy between the original and adversarial signals in the target domain and those in the source domain.Thirdly,contrastive learning constraints were introduced in conjunction with high-confidence pseudo-labels,leveraging source domain class anchor to enhance intra-class compactness and inter-class separability in the target domain.Finally,a consistency constraint was employed to reduce the output discrepancy be-tween original and adversarial signals in the target domain.Experimental results on both public and simulated datasets demonstrate that,compared with existing methods,the proposed method exhibits superior domain adaptability and adver-sarial robustness under various adversarial attacks,effectively enhancing the reliability and security of AMC systems in complex electromagnetic environments.