  Rapid progress in adversarial learning has enabled the generation of
realistic-looking fake visual content. To distinguish between fake and real
visual content, several detection techniques have been proposed. The
performance of most of these techniques however drops off significantly if the
test and the training data are sampled from different distributions. This
motivates efforts towards improving the generalization of fake detectors. Since
current fake content generation techniques do not accurately model the
frequency spectrum of the natural images, we observe that the frequency
spectrum of the fake visual data contains discriminative characteristics that
can be used to detect fake content. We also observe that the information
captured in the frequency spectrum is different from that of the spatial
domain. Using these insights, we propose to complement frequency and spatial
domain features using a two-stream convolutional neural network architecture
called TwoStreamNet. We demonstrate the improved generalization of the proposed
two-stream network to several unseen generation architectures, datasets, and
techniques. The proposed detector has demonstrated significant performance
improvement compared to the current state-of-the-art fake content detectors and
fusing the frequency and spatial domain streams has also improved
generalization of the detector.
