  Advancements in Generative Adversarial Networks (GANs) have the ability to
generate realistic images that are visually indistinguishable from real images.
However, recent studies of the image spectrum have demonstrated that generated
and real images share significant differences at high frequency. Furthermore,
the high-frequency components invisible to human eyes affect the decision of
CNNs and are related to the robustness of it. Similarly, whether the
discriminator will be sensitive to the high-frequency differences, thus
reducing the fitting ability of the generator to the low-frequency components
is an open problem. In this paper, we demonstrate that the discriminator in
GANs is sensitive to such high-frequency differences that can not be
distinguished by humans and the high-frequency components of images are not
conducive to the training of GANs. Based on these, we propose two preprocessing
methods eliminating high-frequency differences in GANs training: High-Frequency
Confusion (HFC) and High-Frequency Filter (HFF). The proposed methods are
general and can be easily applied to most existing GANs frameworks with a
fraction of the cost. The advanced performance of the proposed method is
verified on multiple loss functions, network architectures, and datasets.
