The risk of language models unintentionally reproducing copyrighted material from their training data has led to the development of various protective measures. In this paper, we propose model fusion as an effective solution to safeguard against copyright infringement. In particular, we introduce Copyright-Protecting Fusion (CP-Fuse), an algorithm that adaptively combines language models to minimize the reproduction of protected materials. CP-Fuse is inspired by the recently proposed Near-Access Free (NAF) framework and additionally incorporates a desirable balancing property that we demonstrate prevents the reproduction of memorized training data. Our results show that CP-Fuse significantly reduces the memorization of copyrighted content while maintaining high-quality text and code generation. Furthermore, we demonstrate how CP-Fuse can be integrated with other techniques for enhanced protection.
University of New South Wales（新南威尔士大学）；The Australian National University（澳大利亚国立大学）；CSIRO’s Data61（澳大利亚联邦科学与工业研究组织Data61研究所）
Generative AI (e.g., Generative Adversarial Networks - GANs) has become increasingly popular in recent years. However, Generative AI introduces significant concerns regarding the protection of Intellectual Property Rights (IPR) (resp. model accountability) pertaining to images (resp. toxic images) and models (resp. poisoned models) generated. In this paper, we propose an evaluation framework to provide a comprehensive overview of the current state of the copyright protection measures for GANs, evaluate their performance across a diverse range of GAN architectures, and identify the factors that…
Copyright Protection in Generative AI: A Technical Perspective
Michigan State University（密歇根州立大学）；School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；International Center of Future Science, Jilin University（吉林大学未来科学国际中心）；Stanford University（斯坦福大学）；Sony AI（索尼人工智能）；Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, MOE（教育部知识驱动人机智能工程研究中心）
Cornell University（康奈尔大学）；National University of Singapore（新加坡国立大学）
Machine learning as a service (MLaaS), and algorithm marketplaces are on a rise. Data holders can easily train complex models on their data using third party provided learning codes. Training accurate ML models requires massive labeled data and advanced learning algorithms. The resulting models are considered as intellectual property of the model owners and their copyright should be protected. Also, MLaaS needs to be trusted not to embed secret information about the training data into the model, such that it could be later retrieved when the model is deployed. In this paper, we present \emph{m…
On Provable Copyright Protection for Generative Models
Harvard School of Engineering and Applied Sciences（哈佛大学工程与应用科学学院）；Kempner Institute for the Study of Natural and Artificial Intelligence（肯普纳自然与人工智能研究所）
Domain Watermark: Effective and Harmless Dataset Copyright Protection is Closed at Hand
University of Maryland（马里兰大学）；ZJU-Hangzhou Global Scientific and Technological Innovation Center（浙江大学杭州国际科创中心）；Zhejiang University（浙江大学）；Northwestern University（西北大学）；Tsinghua University（清华大学）；UC Riverside（加州大学河滨分校）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Chicago（芝加哥大学）
Digger: Detecting Copyright Content Mis-usage in Large Language Model Training
Beijing University of Posts and Telecommunications（北京邮电大学）；Nanyang Technological University（南洋理工大学）；Huazhong University of Science and Technology（华中科技大学）；The University of New South Wales（新南威尔士大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））
Creating a state-of-the-art deep-learning system requires vast amounts of data, expertise, and hardware, yet research into embedding copyright protection for neural networks has been limited. One of the main methods for achieving such protection involves relying on the susceptibility of neural networks to backdoor attacks, but the robustness of these tactics has been primarily evaluated against pruning, fine-tuning, and model inversion attacks. In this work, we propose a neural network "laundering" algorithm to remove black-box backdoor watermarks from neural networks even when the adversary h…
