  Recently deep neural networks (DNNs) have achieved significant success in
real-world image super-resolution (SR). However, adversarial image samples with
quasi-imperceptible noises could threaten deep learning SR models. In this
paper, we propose a robust deep learning framework for real-world SR that
randomly erases potential adversarial noises in the frequency domain of input
images or features. The rationale is that on the SR task clean images or
features have a different pattern from the attacked ones in the frequency
domain. Observing that existing adversarial attacks usually add high-frequency
noises to input images, we introduce a novel random frequency mask module that
blocks out high-frequency components possibly containing the harmful
perturbations in a stochastic manner. Since the frequency masking may not only
destroys the adversarial perturbations but also affects the sharp details in a
clean image, we further develop an adversarial sample classifier based on the
frequency domain of images to determine if applying the proposed mask module.
Based on the above ideas, we devise a novel real-world image SR framework that
combines the proposed frequency mask modules and the proposed adversarial
classifier with an existing super-resolution backbone network. Experiments show
that our proposed method is more insensitive to adversarial attacks and
presents more stable SR results than existing models and defenses.
