Wavelet-Guided Frequency-Domain Adaptive Learning: Balancing Adversarial Defense and High-Fidelity Image Reconstruction

Deep learning has achieved remarkable success across diverse domains, including image recognition, segmentation, and restoration. However, adversarial attacks—where imperceptible perturbations are introduced to normal images to mislead models—pose significant security risks to deep neural networks, underscoring the critical need for robust adversarial defense strategies. Image reconstruction-based defense is widely adopted for its downstream model independence and flexibility, yet existing methods often prioritize defense effectiveness at the expense of image quality, limiting their applicability in high-stakes scenarios such as medical diagnosis and remote sensing. To address this imbalance, we propose WS-Net, a wavelet-guided frequency-domain adaptive denoising network. Leveraging the non-uniform distribution of adversarial noise, WS-Net decomposes images into low-frequency and high-frequency components via Discrete Wavelet Transform (DWT), applying denoising exclusively to high-frequency components to preserve critical structural information. At its core is the Swin Network Denoiser (SND), constructed with Swin Transformer-based blocks that utilize shifted window self-attention to accurately distinguish noise from image details. A multi-level loss function—combining pixel-level L1 loss, feature-level perceptual loss, style loss, and adversarial loss—constrains the model from multiple dimensions. Experimental results on the ImageNet dataset demonstrate that WS-Net outperforms state-of-the-art models, achieving a top-1 accuracy of 71.4% on clean images, 70.2% under Gaussian noise, and superior defense performance across nine adversarial attacks (e.g., 68.5% accuracy against CW attacks, 67.9% against DeepFool). Notably, WS-Net achieves a Peak Signal-to-Noise Ratio (PSNR) of 30.2 dB and a Structural Similarity Index (SSIM) of 0.87, balancing defense robustness and image fidelity effectively. The code for WS-Net is available at https://github.com/faaatwang/WS_Net.git .