  The quality of image generation and manipulation is reaching impressive
levels, making it increasingly difficult for a human to distinguish between
what is real and what is fake. However, deep networks can still pick up on the
subtle artifacts in these doctored images. We seek to understand what
properties of fake images make them detectable and identify what generalizes
across different model architectures, datasets, and variations in training. We
use a patch-based classifier with limited receptive fields to visualize which
regions of fake images are more easily detectable. We further show a technique
to exaggerate these detectable properties and demonstrate that, even when the
image generator is adversarially finetuned against a fake image classifier, it
is still imperfect and leaves detectable artifacts in certain image patches.
Code is available at https://chail.github.io/patch-forensics/.
