Seeing Without Exposing: Adaptive Privacy Control for Open-World, Context-Hungry MLLMs 事件

Seeing Without Exposing: Adaptive Privacy Control for Open-World, Context-Hungry MLLMs arXiv:2606.07175v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have raised new privacy challenges. On the data side, user-provided inputs often include unpredictable sensitive information; while on the downstream task side, model reasoning depends on rich visual context that may itself be privacy-sensitive. Existing privacy protection methods, however, rely on predefined sensitive ca