  With advancements in AI-generated images coming on a continuous basis, it is
increasingly difficult to distinguish traditionally-sourced images (e.g.,
photos, artwork) from AI-generated ones. Previous detection methods study the
generalization from a single generator to another in isolation. However, in
reality, new generators are released on a streaming basis. We study
generalization in this setting, training on N models and testing on the next
(N+k), following the historical release dates of well-known generation methods.
Furthermore, images increasingly consist of both real and generated components,
for example through image inpainting. Thus, we extend this approach to pixel
prediction, demonstrating strong performance using automatically-generated
inpainted data. In addition, for settings where commercial models are not
publicly available for automatic data generation, we evaluate if pixel
detectors can be trained solely on whole synthetic images.
