Regeneration Based Training-free Attribution of Fake Images Generated by
  Text-to-Image Generative Models
Meiling Li Zhenxing Qian Xinpeng Zhang
Text-to-image generative models have recently garnered significant attention
due to their ability to generate images based on prompt descriptions. While
these models have shown promising performance, concerns have been raised
regarding the potential misuse of the generated fake images. In response to
this, we have presented a simple yet effective training-free method to
attribute fake images generated by text-to-image models to their source models.
Given a test image to be attributed, we first inverse the textual prompt of the
image, and then put the reconstructed prompt into different candidate models to
regenerate candidate fake images. By calculating and ranking the similarity of
the test image and the candidate images, we can determine the source of the
image. This attribution allows model owners to be held accountable for any
misuse of their models. Note that our approach does not limit the number of
candidate text-to-image generative models. Comprehensive experiments reveal
that (1) Our method can effectively attribute fake images to their source
models, achieving comparable attribution performance with the state-of-the-art
method; (2) Our method has high scalability ability, which is well adapted to
real-world attribution scenarios. (3) The proposed method yields satisfactory
robustness to common attacks, such as Gaussian blurring, JPEG compression, and
Resizing. We also analyze the factors that influence the attribution
performance, and explore the boost brought by the proposed method as a plug-in
to improve the performance of existing SOTA. We hope our work can shed some
light on the solutions to addressing the source of AI-generated images, as well
as to prevent the misuse of text-to-image generative models.