In the field of electromagnetic information security, the detection of electromagnetic leakage of red signals is severely affected by electromagnetic interference. Traditional denoising methods exhibit limitations when dealing with non-stationary signals and complex noise environments. In this study, a denoising method based on generative adversarial network (GAN) is proposed, and efficient noise reduction is achieved through adversarial learning between the generator and discriminator. To address the non-stationary characteristics of electromagnetic signals, a time-frequency dual-domain attention mechanism (TF-DAM) is designed. The generator adopts an improved U-Net architecture that incorporates TF-DAM and integrates residual networks and dropout layers to enhance generalization capability. The encoder-decoder structure and skip connections are utilized to preserve signal details. During training, a dynamic loss-weight adjustment strategy is employed to improve training efficiency and denoising performance. Experimental results demonstrate that the proposed method outperforms traditional approaches in terms of signal-to-noise ratio (SNR) improvement and detail preservation, exhibiting superior performance in nonstationary signal processing. This study provides a novel solution for electromagnetic signal denoising, demonstrating high practical application value.