  The rapid progression of Generative Adversarial Networks (GANs) has raised a
concern of their misuse for malicious purposes, especially in creating fake
face images. Although many proposed methods succeed in detecting GAN-based
synthetic images, they are still limited by the need for large quantities of
the training fake image dataset and challenges for the detector's
generalizability to unknown facial images. In this paper, we propose a new
approach that explores the asynchronous frequency spectra of color channels,
which is simple but effective for training both unsupervised and supervised
learning models to distinguish GAN-based synthetic images. We further
investigate the transferability of a training model that learns from our
suggested features in one source domain and validates on another target domains
with prior knowledge of the features' distribution. Our experimental results
show that the discrepancy of spectra in the frequency domain is a practical
artifact to effectively detect various types of GAN-based generated images.
