FDNet: A Frequency-Aware Decomposition Network for Robust Face Super-Resolution Against Adversarial Attacks

Jia Wang, Peipei Li, Liuyu Xiang, Rui Wang, Zhili Zhang, Qing Tian, Zhaofeng He

Abstract: Face super-resolution (FSR) is a crucial step in the face analysis pipeline, achieving remarkable progress by applying deep neural networks (DNNs). However, DNN-based FSR models are not robust enough and may suffer significant performance degradation due to subtle adversarial perturbations. In addition, the high-frequency details of images restored by existing models are insufficient, especially at large upsampling factors. In this paper, we propose a frequency-aware decomposition network (FDNet) for robust face super-resolution, which aims to defend against adversarial attacks and obtain face images with fidelity. Observing that the noise introduced by adversarial attacks is often intricately mixed with the high-frequency information of the input image, we decompose and process the features of different frequencies separately to eliminate harmful perturbations and enhance high-frequency information. Specifically, by leveraging the frequency-aware capability of empirical mode decomposition (EMD), we propose an EMD-based multi-branch structure. The framework implicitly compels different branches to adaptively extract features from distinct frequency bands, limiting the adversarial noise into decoupled components restricted to specific branches. It also improves the recovery of high-frequency information, which is conducive to producing more credible results. Furthermore, we introduce a high-frequency noise suppressor capable of randomly eliminating imperceptible noise in the high-frequency components. Quantitative and qualitative results demonstrate the superior robustness of our proposed method against adversarial attacks, showing better fidelity in image reconstruction compared to state-of-the-art FSR methods, especially for upscaling factors of 8 and 16.

External IDs:doi:10.1109/tifs.2025.3574956