What makes fake images detectable? Understanding properties that generalize

Understand

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.