Detecting Origin Attribution for Text-to-Image Diffusion Models
This work addresses fake image detection and attribution for AI-generated content, providing incremental insights into discernible hyperparameters and visual traces.
The paper tackled the problem of attributing generated images to specific text-to-image diffusion models, revealing that initialization seeds are highly detectable and that training on style representations outperforms RGB-based attribution.
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.