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/.

Figure 1: Learned latent space trajectories in generative adversarial networks correspond to visual transformations like camera shift and zoom. These transformations can change the distribution of generated data, but only so much -biases in the data, like centered objects, reveal themselves as objects get "stuck" at the image borders when we try to shift them out of frame. Take the "steering wheel", drive in the latent space, and explore the natural image manifold via generative transformations!

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