Diffusion Models (DMs) have shown remarkable capabilities in various
image-generation tasks. However, there are growing concerns that DMs could be
used to imitate unauthorized creations and thus raise copyright issues. To
address this issue, we propose a novel framework that embeds personal
watermarks in the generation of adversarial examples. Such examples can force
DMs to generate images with visible watermarks and prevent DMs from imitating
unauthorized images. We construct a generator based on conditional adversarial
networks and design three losses (adversarial loss, GAN loss, and perturbation
loss) to generate adversarial examples that have subtle perturbation but can
effectively attack DMs to prevent copyright violations. Training a generator
for a personal watermark by our method only requires 5-10 samples within 2-3
minutes, and once the generator is trained, it can generate adversarial
examples with that watermark significantly fast (0.2s per image). We conduct
extensive experiments in various conditional image-generation scenarios.
Compared to existing methods that generate images with chaotic textures, our
method adds visible watermarks on the generated images, which is a more
straightforward way to indicate copyright violations. We also observe that our
adversarial examples exhibit good transferability across unknown generative
models. Therefore, this work provides a simple yet powerful way to protect
copyright from DM-based imitation.

提出了一种将个人水印嵌入敌对实例生成中的新框架，以生成具有可见水印的图像，防止敌对模型模仿未授权图像，并在各种条件图像生成场景进行广泛实验，证明了敌对实例具有良好的传递性，从而提供了一种保护基于扩散模型的版权的简单而有效的方法。

水印嵌入的对抗性样本用于抵抗扩散模型的版权保护

Watermark-embedded Adversarial Examples for Copyright Protection against  Diffusion Models

Language models may memorize more than just facts, including entire chunks of
texts seen during training. Fair use exemptions to copyright laws typically
allow for limited use of copyrighted material without permission from the
copyright holder, but typically for extraction of information from copyrighted
materials, rather than {\em verbatim} reproduction. This work explores the
issue of copyright violations and large language models through the lens of
verbatim memorization, focusing on possible redistribution of copyrighted text.
We present experiments with a range of language models over a collection of
popular books and coding problems, providing a conservative characterization of
the extent to which language models can redistribute these materials. Overall,
this research highlights the need for further examination and the potential
impact on future developments in natural language processing to ensure
adherence to copyright regulations. Code is at
https://github.com/coastalcph/CopyrightLLMs.

本研究通过对语言模型的逐字记忆进行探索，重点关注版权文字的再分发可能性，通过对一系列热门图书和编码问题进行实验，提供了个人估计的语言模型再分发这些材料的程度。总体而言，本研究强调了进一步研究的必要性，以及对版权法规遵守的潜在影响和未来自然语言处理发展的潜在影响。

侵权与大型语言模型

Copyright Violations and Large Language Models

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。