  Modern text-to-image (T2I) diffusion models can generate images with
remarkable realism and creativity. These advancements have sparked research in
fake image detection and attribution, yet prior studies have not fully explored
the practical and scientific dimensions of this task. In addition to
attributing images to 12 state-of-the-art T2I generators, we provide extensive
analyses on what inference stage hyperparameters and image modifications are
discernible. Our experiments reveal that initialization seeds are highly
detectable, along with other subtle variations in the image generation process
to some extent. We further investigate what visual traces are leveraged in
image attribution by perturbing high-frequency details and employing mid-level
representations of image style and structure. Notably, altering high-frequency
information causes only slight reductions in accuracy, and training an
attributor on style representations outperforms training on RGB images. Our
analyses underscore that fake images are detectable and attributable at various
levels of visual granularity than previously explored.
