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

Summary

WS-Net is a wavelet-guided frequency-domain adaptive denoising network for defending image classifiers against adversarial perturbations. It applies Discrete Wavelet Transform (DWT) and denoises only high-frequency components, using a Swin Network Denoiser (SND) trained with a multi-level loss. On ImageNet it reports 71.4% top-1 accuracy on clean images, 68.5% against CW attacks and 67.9% against DeepFool, with PSNR of 30.2 dB and SSIM of 0.87.

Related items

• InfoDoes Target Alignment Mean Target Recovery? An Evidence-Ladder Study of Adversarial Claims on Contrastive EncodersSimilar attack · Arxiv (cs.RO + cs.CV security)
• InfoBRANCH: Bypassing Multi-Scanner AI GuardrailsSimilar attack · Arxiv (cs.CR + cs.CL + cs.LG)
• InfoDetecting Adversarial Images through Response Profiles of Vision-Language ModelsSimilar attack · Arxiv (cs.RO + cs.CV security)
• InfoGraphRectify: Graph-Based Transfer of Adversarial Example Detectors Across Neural NetworksSimilar attack · Arxiv (cs.RO + cs.CV security)
• InfoVCR-Bench: A Modular Open-Source Benchmark for Video Classification RobustnessSimilar attack · Arxiv (cs.RO + cs.CV security)