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 address copyright issues, the capability of large vision-language models (LVLMs) to detect copyright infringements remains largely unexplored. In this work, we focus on evaluating the copyright detection abilities of state-of-the-art LVLMs using a various set of image samples. Recognizing the absence of a comprehensive dataset that includes both IP-infringement samples and ambiguous non-infringement negative samples, we construct a benchmark dataset comprising positive samples that violate the copyright protection of well-known IP figures, as well as negative samples that resemble these figures but do not raise copyright concerns. This dataset is created using advanced prompt engineering techniques. We then evaluate leading LVLMs using our benchmark dataset. Our experimental results reveal that LVLMs are prone to overfitting, leading to the misclassification of some negative samples as IP-infringement cases. In the final section, we analyze these failure cases and propose potential solutions to mitigate the overfitting problem.
Who Owns the Knowledge? Copyright, GenAI, and the Future of Academic Publishing
Centre for Science and Technology Studies (CWTS), Leiden University（莱顿大学科学技术研究中心（CWTS））；Institute for the Study of Science, Russian Academy of Sciences（俄罗斯科学院科学研究所）
The KL3M Data Project: Copyright-Clean Training Resources for Large Language Models
Institute for the Advancement of Legal and Ethical AI (ALEA Institute)（法律与伦理AI推进研究所（ALEA研究所））；CodeX—The Stanford Center for Legal Informatics（CodeX——斯坦福法律信息学中心）；Illinois Tech—Chicago Kent College of Law（伊利诺伊理工大学芝加哥肯特法学院）；Center for Legal Technology & Data Science, Bucerius Law School（布塞留斯法学院法律技术与数据科学中心）
SoK: Large Language Model Copyright Auditing via Fingerprinting
The State Key Laboratory of Blockchain and Data Security（区块链与数据安全国家重点实验室）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新技术区（滨江）区块链与数据安全研究院）；Nanyang Technological University（南洋理工大学）；City University of Hong Kong（香港城市大学）；Sun Yat-Sen University（中山大学）
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学人工智能学院）
AMCR: A Framework for Assessing and Mitigating Copyright Risks in Generative Models
Florida International University, Miami, Florida, United States（佛罗里达国际大学）；Guangdong University of Foreign Studies, Guangzhou, Guangdong, China（广东外语外贸大学）；Carnegie Mellon University, Pittsburgh, Pennsylvania, United States（卡内基梅隆大学）
Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Survey
Rutgers University（罗格斯大学）；University of Technology Sydney（悉尼科技大学）；Ohio State University（俄亥俄州立大学）；University of Notre Dame（圣母大学）；Washington University in St. Louis（圣路易斯华盛顿大学）；University of Chicago（芝加哥大学）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）；Georgia Institute of Technology（佐治亚理工学院）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Carnegie Mellon University（卡内基梅隆大学）；Stony Brook University（石溪大学）；University of Connecticut（康涅狄格大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Boise State University（博伊西州立大学）
TWIG: Two-Step Image Generation using Segmentation Masks in Diffusion Models
BRAC University（布拉克大学）；University of Delaware（德雷塞尔大学）；University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）；ADAPT SFI Research Center（ADAPT SFI研究中心）；Insight SFI Research Center for Data Analytics（Insight SFI数据分析研究中心）；Dublin City University（都柏林城市大学）
With the immense number of videos being uploaded to the video sharing sites, issue of copyright infringement arises with uploading of illicit copies or transformed versions of original video. Thus safeguarding copyright of digital media has become matter of concern. To address this concern, it is obliged to have a video copy detection system which is sufficiently robust to detect these transformed videos with ability to pinpoint location of copied segments. This paper outlines recent advancement in content based video copy detection, mainly focusing on different visual features employed by vid…
CGI-DM: Digital Copyright Authentication for Diffusion Models via Contrasting Gradient Inversion
Shanghai Jiao Tong University（上海交通大学）；Queen’s University Belfast（贝尔法斯特女王大学）；University of Southern California（南加州大学）；Louisiana State University（路易斯安那州立大学）
Can Protective Perturbation Safeguard Personal Data from Being Exploited by Stable Diffusion?
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Drexel University（德雷塞尔大学）；Shanghai Innovation Center for Processor Technologies, SHIC（上海处理器技术创新中心）
SWA-LDM: Toward Stealthy Watermarks for Latent Diffusion Models
Software Engineering Institute, East China Normal University（华东师范大学软件工程学院）；School of Computer Science and Technology, Harbin Institute of Technology (Shen Zhen)（哈尔滨工业大学（深圳）计算机科学与技术学院）；College of Integrated Circuits, Zhejiang University（浙江大学集成电路学院）
Not Every Image is Worth a Thousand Words: Quantifying Originality in Stable Diffusion
School of Computer Science, Tel Aviv University（特拉维夫大学计算机科学学院）；Faculty of Law, Tel Aviv University（特拉维夫大学法学院）；School of Electrical Engineering, Tel Aviv University（特拉维夫大学电气工程学院）
Indian Institute of Technology Gandhinagar（甘地纳加尔印度理工学院）
Knowledge of source smartphone corresponding to a document image can be helpful in a variety of applications including copyright infringement, ownership attribution, leak identification and usage restriction. In this letter, we investigate a convolutional neural network-based approach to solve source smartphone identification problem for printed text documents which have been captured by smartphone cameras and shared over messaging platform. In absence of any publicly available dataset addressing this problem, we introduce a new image dataset consisting of 315 images of documents printed in th…
The availability of large-scale face datasets has been key in the progress of face recognition. However, due to licensing issues or copyright infringement, some datasets are not available anymore (e.g. MS-Celeb-1M). Recent advances in Generative Adversarial Networks (GANs), to synthesize realistic face images, provide a pathway to replace real datasets by synthetic datasets, both to train and benchmark face recognition (FR) systems. The work presented in this paper provides a study on benchmarking FR systems using a synthetic dataset. First, we introduce the proposed methodology to generate a …
Unlearnable Examples for Diffusion Models: Protect Data from Unauthorized Exploitation
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Drexel University（德雷塞尔大学）；Shanghai Innovation Center for Processor Technologies, SHIC（上海处理器技术创新中心）
