Improvements in diffusion models have boosted the quality of image generation, which has led researchers, companies, and creators to focus on improving watermarking algorithms. This provision would make it possible to clearly identify the creators of generative art. The main challenges that modern watermarking algorithms face have to do with their ability to withstand attacks and encrypt many unique messages, such as user IDs. In this paper, we present METR: Message Enhanced Tree-Ring, which is an approach that aims to address these challenges. METR is built on the Tree-Ring watermarking algorithm, a technique that makes it possible to encode multiple distinct messages without compromising attack resilience or image quality. This ensures the suitability of this watermarking algorithm for any Diffusion Model. In order to surpass the limitations on the quantity of encoded messages, we propose METR++, an enhanced version of METR. This approach, while limited to the Latent Diffusion Model architecture, is designed to inject a virtually unlimited number of unique messages. We demonstrate its robustness to attacks and ability to encrypt many unique messages while preserving image quality, which makes METR and METR++ hold great potential for practical applications in real-world settings. Our code is available at https://github.com/deepvk/metr
FreqMark: Invisible Image Watermarking via Frequency Based Optimization in Latent Space
University of California, Santa Cruz（加州大学圣克鲁兹分校）；The University of Hong Kong（香港大学）；University of Science and Technology of China（中国科学技术大学）；IEG, Tencent（腾讯IEG）；Fudan University（复旦大学）
Ivannikov Institute for System Programming of the RAS（俄罗斯科学院伊万尼科夫系统编程研究所）；Russian Federation Security Guard Service Federal Academy（俄罗斯联邦警卫局联邦学院）；Ministry of Defence of the Russian Federation（俄罗斯联邦国防部）
Learning Generalizable and Efficient Image Watermarking via Hierarchical Two-Stage Optimization
Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China（深圳先进研究院，电子科学与技术大学）；School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）
DeepForgeSeal: Latent Space-Driven Semi-Fragile Watermarking for Deepfake Detection Using Adversarial Reinforcement Learning
The Signal Processing, Artificial Intelligence and Vision Technologies (SAIVT), Queensland University of Technology, Australia（信号处理、人工智能与视觉技术研究所（SAIVT），昆士兰理工大学）
Towards Better Statistical Understanding of Watermarking LLMs
Imperial College Business School, Imperial College London（帝国理工学院商学院，帝国理工学院）；The University of Sydney Business School, The University of Sydney（悉尼大学商学院，悉尼大学）；Grado Department of Industrial and Systems Engineering, Virginia Tech（弗吉尼亚理工大学工业与系统工程系）
Unforgeable Watermarks for Language Models via Robust Signatures
Paul G. Allen School of Computer Science & Engineering, University of Washington（保罗·G·艾伦计算机科学与工程学院，华盛顿大学）；Data Science Institute, University of Chicago（数据科学研究所，芝加哥大学）
