The rapid progress of generative AI technology has sparked significant
copyright concerns, leading to numerous lawsuits filed against AI developers.
While some studies explore methods to mitigate copyright risks by steering the
outputs of generative models away from those resembling copyrighted data,
little attention has been paid to the question of how much of a resemblance is
undesirable; more original or unique data are afforded stronger protection, and
the threshold level of resemblance for constituting infringement
correspondingly lower. Here, leveraging this principle, we propose a
genericization method that modifies the outputs of a generative model to make
them more generic and less likely to infringe copyright. To achieve this, we
introduce a metric for quantifying the level of originality of data in a manner
that is consistent with the legal framework. This metric can be practically
estimated by drawing samples from a generative model, which is then used for
the genericization process. Experiments demonstrate that our genericization
method successfully modifies the output of a text-to-image generative model so
that it produces more generic, copyright-compliant images.

通过引入量化数据原创性的度量标准，并采用泛化方法，修改生成模型的输出以符合版权要求，实验结果表明该方法可以成功将文本到图像生成模型的输出转化为更通用符合版权的图像。

应对 GenAI 版权问题：原创性估计与泛化

Tackling GenAI Copyright Issues: Originality Estimation and  Genericization

Identifying whether an artwork was used to train a diffusion model is an
important research topic, given the rising popularity of AI-generated art and
the associated copyright concerns. The work approaches this problem from the
membership inference attack (MIA) perspective. We first identify the
limitations of applying existing MIA methods for copyright protection: the
required access of internal U-nets and the choice of non-member datasets for
evaluation. To address the above problems, we introduce a novel black-box
membership inference attack method that operates without needing access to the
model's internal U-net. We then construct a DALL-E generated dataset for a more
comprehensive evaluation. We validate our method across various setups, and our
experimental results outperform previous works.

通过黑盒成员推断攻击方法，在不需要访问模型的内部 U-net 的情况下，构建了一个 DALL-E 生成的数据集进行全面评估，实验结果优于以前的研究。

面向扩散模型的黑盒成员推断攻击

Towards Black-Box Membership Inference Attack for Diffusion Models

The commercialization of diffusion models, renowned for their ability to
generate high-quality images that are often indistinguishable from real ones,
brings forth potential copyright concerns. Although attempts have been made to
impede unauthorized access to copyrighted material during training and to
subsequently prevent DMs from generating copyrighted images, the effectiveness
of these solutions remains unverified. This study explores the vulnerabilities
associated with copyright protection in DMs by introducing a backdoor data
poisoning attack (SilentBadDiffusion) against text-to-image diffusion models.
Our attack method operates without requiring access to or control over the
diffusion model's training or fine-tuning processes; it merely involves the
insertion of poisoning data into the clean training dataset. This data,
comprising poisoning images equipped with prompts, is generated by leveraging
the powerful capabilities of multimodal large language models and text-guided
image inpainting techniques. Our experimental results and analysis confirm the
method's effectiveness. By integrating a minor portion of
non-copyright-infringing stealthy poisoning data into the clean
dataset-rendering it free from suspicion-we can prompt the finetuned diffusion
models to produce copyrighted content when activated by specific trigger
prompts. These findings underline potential pitfalls in the prevailing
copyright protection strategies and underscore the necessity for increased
scrutiny and preventative measures against the misuse of DMs.

通过在精选数据集中插入有毒数据，利用多模式大型语言模型和文本引导的图像修复技术，本研究证实了在触发特定提示时，微调扩散模型可以生成受版权保护的内容，从而揭示了现行版权保护策略中潜在的陷阱，强调了对扩散模型滥用的加强审查和预防措施的必要性。