SigPurifier: A Dual-Domain Diffusion Framework for Adversarial Signal Purification in Wireless Communication

Abstract

With the rapid development of deep learning, its powerful feature extraction capabilities have shown great advantages in wireless signal modulation classification. However, limited by the inherent fragility of the model, deep learning-based modulation classification models are vulnerable to adversarial attacks. Consequently, defense frameworks integrating filtering mechanisms and adversarial learning have been developed. Yet, such approaches persistently confront the dual dilemmas of excessive feature degradation in signal processing or unwarranted dependence on prior knowledge assumptions, ultimately hindering real-world deployment. In this letter, we propose SigPurifier, a diffusion-based framework that eliminates adversarial perturbations in local data structures through forward diffusion while enhancing structural recovery through learned generative priors during signal denoising transitions. The evaluation results show that the proposed algorithm effectively eliminates adversarial attacks while maintaining signal communication quality. Compared to adversarial training methods, our model can increase the accuracy of the modulation classifier from 27.46% to 31.08%.