Regeneration Based Training-free Attribution of Fake Images Generated by Text-to-Image Generative Models

Understand

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.