Recent text-to-image generative models such as Stable Diffusion are extremely adept at mimicking and generating copyrighted content, raising concerns amongst artists that their unique styles may be improperly copied. Understanding how generative models copy "artistic style" is more complex than duplicating a single image, as style is comprised by a set of elements (or signature) that frequently co-occurs across a body of work, where each individual work may vary significantly. In our paper, we first reformulate the problem of "artistic copyright infringement" to a classification problem over image sets, instead of probing image-wise similarities. We then introduce ArtSavant, a practical (i.e., efficient and easy to understand) tool to (i) determine the unique style of an artist by comparing it to a reference dataset of works from 372 artists curated from WikiArt, and (ii) recognize if the identified style reappears in generated images. We leverage two complementary methods to perform artistic style classification over image sets, includingTagMatch, which is a novel inherently interpretable and attributable method, making it more suitable for broader use by non-technical stake holders (artists, lawyers, judges, etc). Leveraging ArtSavant, we then perform a large-scale empirical study to provide quantitative insight on the prevalence of artistic style copying across 3 popular text-to-image generative models. Namely, amongst a dataset of prolific artists (including many famous ones), only 20% of them appear to have their styles be at a risk of copying via simple prompting of today's popular text-to-image generative models.
Solving Copyright Infringement on Short Video Platforms: Novel Datasets and an Audio Restoration Deep Learning Pipeline
Department of MetaBioHealth, Sungkyunkwan University, Korea（韩国成均馆大学代谢生物健康系）；Department of Applied Artificial Intelligence, Sungkyunkwan University, Korea（韩国成均馆大学应用人工智能系）；Department of Computer Science and Engineering, Jaume I University, Spain（西班牙伊萨贝拉大学计算机科学与工程系）
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）
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）
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）
VideoEraser: Concept Erasure in Text-to-Video Diffusion Models
Zhejiang University（浙江大学）；University of California, Los Angeles（加州大学洛杉矶分校）；Palo Alto Networks（帕洛阿尔托网络公司）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Buchmann Faculty of Law, Tel Aviv University（特拉维夫大学法学院）；Blavatnik School of Computer Science（比拉维克计算机科学学院）；AI , Tel Aviv University（人工智能，特拉维夫大学）；School of Electrical and Computer Engineering（电气与计算机工程学院）；Departments of Mathematics, Computer Science（数学与计算机科学系）；Decision Sciences Technion– Israel Institute of Technology（决策科学技术学院——以色列理工学院）
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）
Renmin University of China（中国人民大学）；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）
Rapid pace of generative models has brought about new threats to visual forensics such as malicious personation and digital copyright infringement, which promotes works on fake image attribution. Existing works on fake image attribution mainly rely on a direct classification framework. Without additional supervision, the extracted features could include many content-relevant components and generalize poorly. Meanwhile, how to obtain an interpretable GAN fingerprint to explain the decision remains an open question. Adopting a multi-task framework, we propose a GAN Fingerprint Disentangling Netw…
Unconsciously Forget: Mitigating Memorization; Without Knowing What is being Memorized
RWTH Aachen University（亚琛工业大学）；National University of Singapore（新加坡国立大学）；Fraunhofer Institute for Applied Information Technology FIT（弗劳恩霍夫应用信息技术研究所FIT）
