T2SMark: Balancing Robustness and Diversity in Noise-as-Watermark for Diffusion Models
University of Science and Technology of China（中国科学技术大学）；Anhui Province Key Laboratory of Digital Security（安徽省数字安全重点实验室）；National University of Singapore（新加坡国立大学）
Jindong Yang, Han Fang, Weiming Zhang, Nenghai Yu, Kejiang Chen
Diffusion models have advanced rapidly in recent years, producing high-fidelity images while raising concerns about intellectual property protection and the misuse of generative AI. Image watermarking for diffusion models, particularly Noise-as-Watermark (NaW) methods, encode watermark as specific standard Gaussian noise vector for image generation, embedding the infomation seamlessly while maintaining image quality. For detection, the generation process is inverted to recover the initial noise vector containing the watermark before extraction. However, existing NaW methods struggle to balance watermark robustness with generation diversity. Some methods achieve strong robustness by heavily constraining initial noise sampling, which degrades user experience, while others preserve diversity but prove too fragile for real-world deployment. To address this issue, we propose T2SMark, a two-stage watermarking scheme based on Tail-Truncated Sampling (TTS). Unlike prior methods that simply map bits to positive or negative values, TTS enhances robustness by embedding bits exclusively in the reliable tail regions while randomly sampling the central zone to preserve the latent distribution. Our two-stage framework then ensures sampling diversity by integrating a randomly generated session key into both encryption pipelines. We evaluate T2SMark on diffusion models with both U-Net and DiT backbones. Extensive experiments show that it achieves an optimal balance between robustness and diversity. Our code is available at \href{https://github.com/0xD009/T2SMark}{https://github.com/0xD009/T2SMark}.
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）
Npix2Cpix: A GAN-Based Image-to-Image Translation Network With Retrieval- Classification Integration for Watermark Retrieval From Historical Document Images
BRAC University（BRAC大学）；The University of Texas at Dallas（德克萨斯大学达拉斯分校）
Wroclaw University of Science and Technology（弗罗茨瓦夫理工大学）；Vestigit
In this paper we present a novel deep framework for a watermarking - a technique of embedding a transparent message into an image in a way that allows retrieving the message from a (perturbed) copy, so that copyright infringement can be tracked. For this technique, it is essential to extract the information from the image even after imposing some digital processing operations on it. Our framework outperforms recent methods in the context of robustness against not only spectrum of attacks (e.g. rotation, resizing, Gaussian smoothing) but also against compression, especially JPEG. The bit accura…
University of New South Wales（新南威尔士大学）；Jiangnan University（江南大学）；University of Technology Sydney（悉尼科技大学）；University of California San Diego（加州大学圣地亚哥分校）；CSIRO’s Data61（澳大利亚联邦科学与工业研究组织Data61）
Zero-Shot Learning (ZSL) aims to transfer classification capability from seen to unseen classes. Recent methods have proved that generalization and specialization are two essential abilities to achieve good performance in ZSL. However, focusing on only one of the abilities may result in models that are either too general with degraded classification ability or too specialized to generalize to unseen classes. In this paper, we propose an end-to-end network, termed as BGSNet, which equips and balances generalization and specialization abilities at the instance and dataset level. Specifically, BG…
Class Balancing Diversity Multimodal Ensemble for Alzheimer's Disease Diagnosis and Early Detection
Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma（计算机系统与生物信息学单位，工程系，罗马生物医学大学）；Operative Research Unit of Neurology, Fondazione Policlinico Universitario Campus Bio-Medico（神经学操作研究单位，大学医学研究院生物医学 Campus）；Eustema S.p.A., Research and Development Centre（Eustema 公司，研发中心）；Department of Diagnostic and Intervention, Radiation Physics, Biomedical Engineering, Umeå University（诊断与介入部门，辐射物理，生物医学工程，乌梅大学）
Balancing Uncertainty and Diversity of Samples: Leveraging Diversity of Least, High Confidence Samples for Effective Active Learning
School of Computer Science and Engineering, RV University（计算机科学与工程学院，RV大学）；School of Engineering & Technology, Vidyashilp University（工程与技术学院，Vidyashilp大学）；Shiv Nadar Institution of Eminence（Shiv Nadar卓越研究院）
本文提出了一种新的混合采样方法，通过同时选择容易和困难的样本，结合多样性，以提高主动学习的效果。实验表明，所提出的Least Confident and Diverse (LCD)方法在性能上优于现有方法，通过选择不确定且多样的实例，帮助模型学习更明显的特征。
Rethinking Mitosis Detection: Towards Diverse Data and Feature Representation
South China University of Technology（华南理工大学）；Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences)（广东省人民医院（广东省医学科学院））；Southern Medical University（南方医科大学）；Nanfang Hospital（南方医院）；Guangdong Provincial People’s Hospital Ganzhou Hospital（广东省人民医院赣州医院）；Guilin University of Electronic Technology（桂林电子科技大学）
Recently, the state-of-the-art models for image captioning have overtaken human performance based on the most popular metrics, such as BLEU, METEOR, ROUGE, and CIDEr. Does this mean we have solved the task of image captioning? The above metrics only measure the similarity of the generated caption to the human annotations, which reflects its accuracy. However, an image contains many concepts and multiple levels of detail, and thus there is a variety of captions that express different concepts and details that might be interesting for different humans. Therefore only evaluating accuracy is not s…
