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Large scale text-to-image generation models can memorize and reproduce their training dataset. Since the training dataset often contains copyrighted material, reproduction of training dataset poses a copyright infringement risk, which could…

机器学习 · 计算机科学 2025-12-18 Neeraj Sarna , Yuanyuan Li , Michael von Gablenz

The popularity of visual generative AI models like DALL-E 3, Stable Diffusion XL, Stable Video Diffusion, and Sora has been increasing. Through extensive evaluation, we discovered that the state-of-the-art visual generative models can…

计算机视觉与模式识别 · 计算机科学 2024-06-10 Zhenting Wang , Chen Chen , Vikash Sehwag , Minzhou Pan , Lingjuan Lyu

The rapid progress of generative AI technology has sparked significant copyright concerns, leading to numerous lawsuits filed against AI developers. Notably, generative AI's capacity for generating images of copyrighted characters has been…

机器学习 · 计算机科学 2025-04-01 Hiroaki Chiba-Okabe , Weijie J. Su

Generative models have achieved impressive results in text to image tasks, significantly advancing visual content creation. However, this progress comes at a cost, as such models rely heavily on large-scale training data and may…

机器学习 · 计算机科学 2025-09-03 Zhipeng Yin , Zichong Wang , Avash Palikhe , Zhen Liu , Jun Liu , Wenbin Zhang

Generative artificial intelligence (AI) systems are trained on large data corpora to generate new pieces of text, images, videos, and other media. There is growing concern that such systems may infringe on the copyright interests of…

机器学习 · 计算机科学 2024-09-10 Jiachen T. Wang , Zhun Deng , Hiroaki Chiba-Okabe , Boaz Barak , Weijie J. Su

Generative AI has witnessed rapid advancement in recent years, expanding their capabilities to create synthesized content such as text, images, audio, and code. The high fidelity and authenticity of contents generated by these Deep…

As the capabilities of large language models (LLMs) continue to expand, their usage has become increasingly prevalent. However, as reflected in numerous ongoing lawsuits regarding LLM-generated content, addressing copyright infringement…

Artificial intelligence (AI) model creators commonly attach restrictive terms of use to both their models and their outputs. These terms typically prohibit activities ranging from creating competing AI models to spreading disinformation.…

计算机与社会 · 计算机科学 2024-12-11 Peter Henderson , Mark A. Lemley

Since its introduction in 2022, Generative AI has significantly impacted the art world, from winning state art fairs to creating complex videos from simple prompts. Amid this renaissance, a pivotal issue emerges: should users of Generative…

计算机与社会 · 计算机科学 2024-06-19 Yiyang Mei

Large Generative AI (GAI) models have the unparalleled ability to generate text, images, audio, and other forms of media that are increasingly indistinguishable from human-generated content. As these models often train on publicly available…

计算机与社会 · 计算机科学 2024-06-25 Tanja Šarčević , Alicja Karlowicz , Rudolf Mayer , Ricardo Baeza-Yates , Andreas Rauber

To achieve accurate and unbiased predictions, Machine Learning (ML) models rely on large, heterogeneous, and high-quality datasets. However, this could raise ethical and legal concerns regarding copyright and authorization aspects,…

机器学习 · 计算机科学 2024-10-10 Daniela Gallo , Angelica Liguori , Ettore Ritacco , Luca Caviglione , Fabrizio Durante , Giuseppe Manco

Generative AI is becoming increasingly prevalent in creative fields, sparking urgent debates over how current copyright laws can keep pace with technological innovation. Recent controversies of AI models generating near-replicas of…

机器学习 · 计算机科学 2025-07-01 Archer Amon , Zhipeng Yin , Zichong Wang , Avash Palikhe , Wenbin Zhang

"Does generative AI infringe copyright?" is an urgent question. It is also a difficult question, for two reasons. First, "generative AI" is not just one product from one company. It is a catch-all name for a massive ecosystem of loosely…

计算机与社会 · 计算机科学 2024-03-05 Katherine Lee , A. Feder Cooper , James Grimmelmann

Recent advances in generative models have demonstrated an exceptional ability to produce highly realistic images. However, previous studies show that generated images often resemble the training data, and this problem becomes more severe as…

计算机视觉与模式识别 · 计算机科学 2025-12-15 Er Jin , Yang Zhang , Yongli Mou , Yanfei Dong , Stefan Decker , Kenji Kawaguchi , Johannes Stegmaier

With the rapid deployment of multimodal large language models (MLLMs), disputes regarding model ownership have become increasingly frequent, raising significant concerns about intellectual property protection. In this paper, we propose a…

计算机视觉与模式识别 · 计算机科学 2026-03-31 Chengwei Xia , Fan Ma , Ruijie Quan , Yunqiu Xu , Kun Zhan , Yi Yang

Existing foundation models are trained on copyrighted material. Deploying these models can pose both legal and ethical risks when data creators fail to receive appropriate attribution or compensation. In the United States and several other…

计算机与社会 · 计算机科学 2023-03-30 Peter Henderson , Xuechen Li , Dan Jurafsky , Tatsunori Hashimoto , Mark A. Lemley , Percy Liang

Copyright law focuses on whether a new work is "substantially similar" to an existing one, but generative AI can closely imitate style without copying content, a capability now central to ongoing litigation. We argue that existing…

理论经济学 · 经济学 2026-02-13 Annie Liang , Jay Lu

Generative AI models, renowned for their ability to synthesize high-quality content, have sparked growing concerns over the improper generation of copyright-protected material. While recent studies have proposed various approaches to…

计算机视觉与模式识别 · 计算机科学 2025-02-25 Qipan Xu , Zhenting Wang , Xiaoxiao He , Ligong Han , Ruixiang Tang

Exploring the data sources used to train Large Language Models (LLMs) is a crucial direction in investigating potential copyright infringement by these models. While this approach can identify the possible use of copyrighted materials in…

计算与语言 · 计算机科学 2024-09-24 Weijie Zhao , Huajie Shao , Zhaozhuo Xu , Suzhen Duan , Denghui Zhang

Language models (LMs) derive their capabilities from extensive training on diverse data, including potentially copyrighted material. These models can memorize and generate content similar to their training data, posing potential concerns.…

计算与语言 · 计算机科学 2024-10-14 Boyi Wei , Weijia Shi , Yangsibo Huang , Noah A. Smith , Chiyuan Zhang , Luke Zettlemoyer , Kai Li , Peter Henderson
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