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.

通过反转图像的文本提示并将重构的提示放入不同的候选模型中来重新生成候选伪图片，通过计算并排序测试图片与候选图片的相似性，我们可以确定图片的来源模型，从而使模型的所有者对其模型的任何滥用负有责任。

基于再生的无需训练的文本图像生成模型伪造图像的归因

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

Stable Diffusion is a recent open-source image generation model comparable to
proprietary models such as DALLE, Imagen, or Parti. Stable Diffusion comes with
a safety filter that aims to prevent generating explicit images. Unfortunately,
the filter is obfuscated and poorly documented. This makes it hard for users to
prevent misuse in their applications, and to understand the filter's
limitations and improve it. We first show that it is easy to generate
disturbing content that bypasses the safety filter. We then reverse-engineer
the filter and find that while it aims to prevent sexual content, it ignores
violence, gore, and other similarly disturbing content. Based on our analysis,
we argue safety measures in future model releases should strive to be fully
open and properly documented to stimulate security contributions from the
community.

该研究通过反向工程分析稳定扩散算法的安全过滤器，发现该过滤器只能防止生成性内容而无法防止暴力、血腥等扰人的内容，呼吁未来算法的安全措施应该是完全开放和正确记录的。