The rapid advancement of generative models, facilitating the creation of
hyper-realistic images from textual descriptions, has concurrently escalated
critical societal concerns such as misinformation. Traditional fake detection
mechanisms, although providing some mitigation, fall short in attributing
responsibility for the malicious use of synthetic images. This paper introduces
a novel approach to model fingerprinting that assigns responsibility for the
generated images, thereby serving as a potential countermeasure to model
misuse. Our method modifies generative models based on each user's unique
digital fingerprint, imprinting a unique identifier onto the resultant content
that can be traced back to the user. This approach, incorporating fine-tuning
into Text-to-Image (T2I) tasks using the Stable Diffusion Model, demonstrates
near-perfect attribution accuracy with a minimal impact on output quality. We
rigorously scrutinize our method's secrecy under two distinct scenarios: one
where a malicious user attempts to detect the fingerprint, and another where a
user possesses a comprehensive understanding of our method. We also evaluate
the robustness of our approach against various image post-processing
manipulations typically executed by end-users. Through extensive evaluation of
the Stable Diffusion models, our method presents a promising and novel avenue
for accountable model distribution and responsible use.

本文提出了一种新的模型指纹技术，将唯一标识符印刷到生成内容中，以追溯图像的生成者，即模型的责任人，并在不影响生成内容质量的情况下，在 Text-to-Image（T2I）任务中将微调纳入其中；通过对 Stable Diffusion 模型进行大量评估，本方法为模型分发和负责任使用提供了一种有前途的新途径。

用于文本到图像扩散模型中用户归因和指纹识别的权重调节

WOUAF: Weight Modulation for User Attribution and Fingerprinting in  Text-to-Image Diffusion Models

A method for authorship attribution based on function word adjacency networks
(WANs) is introduced. Function words are parts of speech that express
grammatical relationships between other words but do not carry lexical meaning
on their own. In the WANs in this paper, nodes are function words and directed
edges stand in for the likelihood of finding the sink word in the ordered
vicinity of the source word. WANs of different authors can be interpreted as
transition probabilities of a Markov chain and are therefore compared in terms
of their relative entropies. Optimal selection of WAN parameters is studied and
attribution accuracy is benchmarked across a diverse pool of authors and
varying text lengths. This analysis shows that, since function words are
independent of content, their use tends to be specific to an author and that
the relational data captured by function WANs is a good summary of stylometric
fingerprints. Attribution accuracy is observed to exceed the one achieved by
methods that rely on word frequencies alone. Further combining WANs with
methods that rely on word frequencies alone, results in larger attribution
accuracy, indicating that both sources of information encode different aspects
of authorial styles.

本文介绍了一种基于功能词邻接网络的作者归属度量方法，研究了 WAN 参数的最优选择和不同作者之间的比较，结果显示相对熵是有效的判别方法，同时，功能词邻接网络与词频方法的组合可以进一步提高归属度量的准确度。