Text-to-image diffusion models are nothing but a revolution, allowing anyone, even without design skills, to create realistic images from simple text inputs. With powerful personalization tools like DreamBooth, they can generate images of a specific person just by learning from his/her few reference images. However, when misused, such a powerful and convenient tool can produce fake news or disturbing content targeting any individual victim, posing a severe negative social impact. In this paper, we explore a defense system called Anti-DreamBooth against such malicious use of DreamBooth. The system aims to add subtle noise perturbation to each user's image before publishing in order to disrupt the generation quality of any DreamBooth model trained on these perturbed images. We investigate a wide range of algorithms for perturbation optimization and extensively evaluate them on two facial datasets over various text-to-image model versions. Despite the complicated formulation of DreamBooth and Diffusion-based text-to-image models, our methods effectively defend users from the malicious use of those models. Their effectiveness withstands even adverse conditions, such as model or prompt/term mismatching between training and testing. Our code will be available at https://github.com/VinAIResearch/Anti-DreamBooth.git.
DiffuseKronA: A Parameter Efficient Fine-tuning Method for Personalized Diffusion Models
Indian Institute of Information Technology Guwahati（印度古瓦哈蒂印度信息技术学院）；Hugging Face；National Yang Ming Chiao Tung University（国立阳明交通大学）；IBM Research（IBM研究院）
DreamVTON: Customizing 3D Virtual Try-on with Personalized Diffusion Models
Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）；ByteDance（字节跳动）；Carnegie Mellon University（卡内基梅隆大学）；Research Institute of Multiple Agents and Embodied Intelligence（多智能体与具身智能研究院）；Peng Cheng Laboratory（鹏城实验室）
本文提出 DreamVTON 模型，通过结合个性化扩散模型、Densepose-guided ControlNet 和基于模板的优化机制，分别优化 3D 人体的几何与纹理，解决了个性化模块削弱多视图合成能力的问题，实现了高质量的定制化 3D 虚拟试穿。
Fragile by Design: On the Limits of Adversarial Defenses in Personalized Generation
Department of Computer Science, University of Liverpool, Liverpool, L69 3BX, UK（利兹大学计算机科学系）；WMG, University of Warwick, Coventry, CV4 7AL, UK（沃里克大学WMG）
ID-Cloak: Crafting Identity-Specific Cloaks Against Personalized Text-to-Image Generation
Beijing University of Posts and Telecommunications（北京邮电大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of California, Santa Barbara（加利福尼亚大学圣巴巴拉分校）
Disrupting Diffusion: Token-Level Attention Erasure Attack against Diffusion-based Customization
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）
CUHK（香港中文大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Peking University（北京大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Efficient, Robust, and Anti-Collusion Fingerprinting of Image Diffusion Models
University of Florence（佛罗伦萨大学）；Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）；College of Cyber Security, Jinan University（暨南大学网络空间安全学院）；State Key Laboratory of Internet of Things for Smart City, University of Macau（澳门大学智慧城市物联网国家重点实验室）；Department of Computer and Information Science, Faculty of Science and Technology, University of Macau（澳门大学科技学院计算机与信息科学系）；University of Siena（锡耶纳大学）
Hierarchical Anti-Aesthetics: Protecting Facial Privacy against Customized Diffusion Models
School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）；China Mobile Information Technology Co., Ltd.（中国移动信息科技有限公司）；State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Center for Research on Intelligent Perception and Computing (CRIPAC), Institute of Automation Chinese Academy of Sciences (CASIA)（多模态人工智能系统国家重点实验室（MAIS）、智能感知与计算研究中心（CRIPAC）、中国科学院自动化研究所（CASIA））
Genotype-Conditioned Molecular Generation via Evidence-Grounded Multi-Objective Latent Perturbation in Diffusion Models
Department of Computer Science and Engineering（计算机科学与工程系）；University of Notre Dame（诺克斯大学）；Department of Chemistry and Biochemistry（化学与生物化学系）；Lucy Family Institute for Data & Society（数据与社会学院）
