Image-to-video (I2V) generation models can be misused to animate a single image into a convincing fake video, motivating perturbation-based image protection methods that aim to disrupt such generation. Yet these methods remain difficult to compare: they are reported under inconsistent metrics and generation settings, are often validated only on the single generator they were optimized against, and are evaluated on narrow, single-domain image sets that do not reflect real misuse. To address these challenges, we introduce IPV-Bench (Image Protection against Video generation), the first systematic benchmark for image protection in I2V generation scenarios. IPV-Bench couples a unified protocol that jointly scores protection effectiveness, visual fidelity, and robustness to preprocessing attacks together with IPV-500, a prompt-paired dataset spanning five misuse-relevant domains. Based on this benchmark, we evaluate five representative protection methods across four I2V models covering distinct architectures and both open-source and commercial systems. Extensive experiments show a consistently sobering picture: image protection and video disruption trade off against each other, most methods fail to disrupt generation beyond noise, protection rarely transfers across generators, and the few effective cases are broken by simple preprocessing. We further find that image content governs the perceptual cost of protection but not its benefit: no image domain offers an easier target. Overall, IPV-Bench provides a rigorous, reproducible, and extensible foundation for developing protection methods that work in practice.
DP^2-VL: Private Photo Dataset Protection by Data Poisoning for Vision-Language Models
Shandong University（山东大学）；Shanghai Jiao Tong University（上海交通大学）；Donghua University（东华大学）；Shanghai Normal University（上海师范大学）；East China Normal University（华东师范大学）；Hefei University of Technology（合肥工业大学）
Who Can See Through You? Adversarial Shielding Against VLM-Based Attribute Inference Attacks
East China Normal University（东华大学）；Zhongguancun Academy（中关村学院）；Dept. of Comp. Sci. and Tech., Institute for AI, Tsinghua University（计算机科学与技术系，人工智能研究院，清华大学）
Adversarial Attacks for Good: A Survey of Proactive Protection across the Visual Content Lifecycle
College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；School of Computing and Information Systems, University of Melbourne（墨尔本大学计算与信息系统学院）；School of Computing and Information Systems, Singapore Management University（新加坡管理大学计算与信息系统学院）；Institute of Trustworthy Embodied AI, Fudan University（复旦大学可信具身人工智能研究院）；Department of Computer Science, School of Computing, Institute of Science Tokyo（东京科学大学计算学院计算机科学系）；Sony AI（索尼人工智能公司）
