  The rapid advances in deep generative models over the past years have led to
highly {realistic media, known as deepfakes,} that are commonly
indistinguishable from real to human eyes. These advances make assessing the
authenticity of visual data increasingly difficult and pose a misinformation
threat to the trustworthiness of visual content in general. Although recent
work has shown strong detection accuracy of such deepfakes, the success largely
relies on identifying frequency artifacts in the generated images, which will
not yield a sustainable detection approach as generative models continue
evolving and closing the gap to real images. In order to overcome this issue,
we propose a novel fake detection that is designed to re-synthesize testing
images and extract visual cues for detection. The re-synthesis procedure is
flexible, allowing us to incorporate a series of visual tasks - we adopt
super-resolution, denoising and colorization as the re-synthesis. We
demonstrate the improved effectiveness, cross-GAN generalization, and
robustness against perturbations of our approach in a variety of detection
scenarios involving multiple generators over CelebA-HQ, FFHQ, and LSUN
datasets. Source code is available at
https://github.com/SSAW14/BeyondtheSpectrum.
