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

• URL: http://arxiv.org/abs/2403.01489v1
• Date: Sun, 3 Mar 2024 11:55:49 GMT
• Title: Regeneration Based Training-free Attribution of Fake Images Generated by Text-to-Image Generative Models
• Authors: Meiling Li, Zhenxing Qian, Xinpeng Zhang
• Abstract summary: We present a training-free method to attribute fake images generated by text-to-image models to their source models. By calculating and ranking the similarity of the test image and the candidate images, we can determine the source of the image.
• Score: 39.33821502730661
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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