Text-to-image generative models, especially those based on latent diffusion models (LDMs), have demonstrated outstanding ability in generating high-quality and high-resolution images from textual prompts. With this advancement, various fine-tuning methods have been developed to personalize text-to-image models for specific applications such as artistic style adaptation and human face transfer. However, such advancements have raised copyright concerns, especially when the data are used for personalization without authorization. For example, a malicious user can employ fine-tuning techniques to replicate the style of an artist without consent. In light of this concern, we propose FT-Shield, a watermarking solution tailored for the fine-tuning of text-to-image diffusion models. FT-Shield addresses copyright protection challenges by designing new watermark generation and detection strategies. In particular, it introduces an innovative algorithm for watermark generation. It ensures the seamless transfer of watermarks from training images to generated outputs, facilitating the identification of copyrighted material use. To tackle the variability in fine-tuning methods and their impact on watermark detection, FT-Shield integrates a Mixture of Experts (MoE) approach for watermark detection. Comprehensive experiments validate the effectiveness of our proposed FT-Shield.
Decoder Gradient Shields: A Family of Provable and High-Fidelity Methods Against Gradient-Based Box-Free Watermark Removal
Hong Kong JC STEM Lab of Smart City and Department of Computer Science, City University of Hong Kong（香港JC STEM实验室及城市大学计算机科学系）；Infocomm Technology Cluster, Singapore Institute of Technology（新加坡理工学院信息通信技术集群）；College of Information Science & Electronic Engineering, Zhejiang University（浙江大学信息科学与电子工程学院）；School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科学与技术大学计算机科学与工程学院）；Department of Electronic and Electrical Engineering, University College London（伦敦大学学院电子与电气工程系）
Protecting Your Video Content: Disrupting Automated Video-based LLM Annotations
Tsinghua University（清华大学）；ByteDance（字节跳动）；Digital Center, China Merchants Group Limited（招商银行集团数字中心）；Shenzhen University（深圳大学）；Peng Cheng Laboratory（鹏城实验室）
LVMark: Robust Watermark for Latent Video Diffusion Models
Department of Artificial Intelligence, Korea University, Seoul 02841, Republic of Korea（人工智能系，韩国大学，首尔02841，大韩民国）；Google DeepMind, Mountain View, CA 94043, USA（谷歌DeepMind，山景城，加利福尼亚州94043，美国）
Harnessing Frequency Spectrum Insights for Image Copyright Protection Against Diffusion Models
Zhejiang University（浙江大学）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新区（滨江）区块链与数据安全研究院）；National University of Singapore（新加坡国立大学）；South China University of Technology（华南理工大学）
An Efficient and Effective Watermarking Scheme for the Protection of the Intellectual Property Rights of Video Generative Models
Sun Yat-sen University（中山大学）；Guangdong Provincial Key Laboratory of Information Security Technology（广东省信息安全技术重点实验室）；University of Florence（佛罗伦萨大学）；University of Siena（锡耶纳大学）；Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院））
Cert-LAS: Toward Certified Model Ownership Verification for Text-to-Image Diffusion Models via Layer-Adaptive Smoothing
Generative AI Lab, College of Computing；Data Science, Nanyang Technological University, Singapore；Department of Computer Science；Engineering, Texas A\&M University, USA
University of Malaya（马来亚大学）；WeBank（微众银行）；Hong Kong University of Science and Technology（香港科技大学）
Ever since Machine Learning as a Service (MLaaS) emerges as a viable business that utilizes deep learning models to generate lucrative revenue, Intellectual Property Right (IPR) has become a major concern because these deep learning models can easily be replicated, shared, and re-distributed by any unauthorized third parties. To the best of our knowledge, one of the prominent deep learning models - Generative Adversarial Networks (GANs) which has been widely used to create photorealistic image are totally unprotected despite the existence of pioneering IPR protection methodology for Convolutio…
University of Burgundy（勃艮第大学）；Higher School of Technology（高等技术学院）；Faculty of Sciences, University of Mohammed V（穆罕默德五世大学理学院）
Due to the current progress in Internet, digital contents (video, audio and images) are widely used. Distribution of multimedia contents is now faster and it allows for easy unauthorized reproduction of information. Digital watermarking came up while trying to solve this problem. Its main idea is to embed a watermark into a host digital content without affecting its quality. Moreover, watermarking can be used in several applications such as authentication, copy control, indexation, Copyright protection, etc. In this paper, we propose a blind robust image watermarking approach as a solution to …
Monash University Malaysia（马来西亚莫纳什大学）；Okayama University（冈山大学）
High-dynamic range (HDR) images are circulated rapidly over the internet with risks of being exploited for unauthorized usage. To protect these images, some HDR image based watermarking (HDR-IW) methods were put forward. However, they inherited the same problem faced by conventional IW methods for standard dynamic range (SDR) images, where only trade-offs among conflicting requirements are managed instead of simultaneous improvement. In this paper, a novel saliency (eye-catching object) detection based trade-off independent HDR-IW is proposed, to simultaneously improve robustness, imperceptibi…
To See is Not to Learn: Protecting Multimodal Data from Unauthorized Fine-Tuning of Large Vision-Language Model
School of Computing；Augmented Intelligence, Arizona State University, Tempe, AZ, USA；Department of Computer Science；Engineering, Texas A\&M University, College Station, TX, USA
