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.

对现代文本到图像 (T2I) 扩散模型进行了研究，这些模型可以生成非常逼真和具有创造力的图像。我们提供了广泛的分析，关于哪些推理阶段的超参数和图像修改是可辨认的。我们进一步调查了图像归属所依赖的视觉痕迹，通过扰乱高频细节和使用图像风格和结构的中级表示。我们的分析强调，与以前探索的视觉粒度相比，伪造图像在各个层面上都是可检测且可归属的。