To support various applications, business owners often seek the customized
models that are obtained by fine-tuning a pre-trained LLM through the API
provided by LLM owners or cloud servers. However, this process carries a
substantial risk of model misuse, potentially resulting in severe economic
consequences for business owners. Thus, safeguarding the copyright of these
customized models during LLM fine-tuning has become an urgent practical
requirement, but there are limited existing solutions to provide such
protection. To tackle this pressing issue, we propose a novel watermarking
approach named "Double-I watermark". Specifically, based on the instruct-tuning
data, two types of backdoor data paradigms are introduced with trigger in the
instruction and the input, respectively. By leveraging LLM's learning
capability to incorporate customized backdoor samples into the dataset, the
proposed approach effectively injects specific watermarking information into
the customized model during fine-tuning, which makes it easy to inject and
verify watermarks in commercial scenarios. We evaluate the proposed "Double-I
watermark" under various fine-tuning methods, demonstrating its harmlessness,
robustness, uniqueness, imperceptibility, and validity through both theoretical
analysis and experimental verification.

为了保护商业定制模型的版权，在 LMM fine-tuning 过程中引入了一种名为 “Double-I watermark” 的新水印方法，通过两种类型的后门数据范例，有效地将特定的水印信息注入商业定制模型，并在各种 fine-tuning 方法下进行了评估。

双 I 水印：保护 LLM 微调模型的版权

Double-I Watermark: Protecting Model Copyright for LLM Fine-tuning

Text-to-image generative models based on latent diffusion models (LDM) have
demonstrated their outstanding ability in generating high-quality and
high-resolution images according to language prompt. Based on these powerful
latent diffusion models, various fine-tuning methods have been proposed to
achieve the personalization of text-to-image diffusion models such as artistic
style adaptation and human face transfer. However, the unauthorized usage of
data for model personalization has emerged as a prevalent concern in relation
to copyright violations. For example, a malicious user may use the fine-tuning
technique to generate images which mimic the style of a painter without his/her
permission. In light of this concern, we have proposed FT-Shield, a
watermarking approach specifically designed for the fine-tuning of
text-to-image diffusion models to aid in detecting instances of infringement.
We develop a novel algorithm for the generation of the watermark to ensure that
the watermark on the training images can be quickly and accurately transferred
to the generated images of text-to-image diffusion models. A watermark will be
detected on an image by a binary watermark detector if the image is generated
by a model that has been fine-tuned using the protected watermarked images.
Comprehensive experiments were conducted to validate the effectiveness of
FT-Shield.

基于潜在扩散模型的文本生成图像模型通过语言提示展现出了出色的能力，但由于版权侵犯的普遍担忧，本文提出了一种针对文本生成图像模型的水印方法 FT-Shield，以帮助检测侵权的情况。经过全面实验证明了 FT-Shield 的有效性。