When Visual Privacy Protection Meets Multimodal Large Language Models
The emergence of Multimodal Large Language Models (MLLMs) and the widespread usage of MLLM cloud services such as GPT-4V raised great concerns about privacy leakage in visual data. As these models are typically deployed in cloud services, users are required to submit their images and videos, posing serious privacy risks. However, how to tackle such privacy concerns is an under-explored problem. Thus, in this paper, we aim to conduct a new investigation to protect visual privacy when enjoying the convenience brought by MLLM services. We address the practical case where the MLLM is a "black box", i.e., we only have access to its input and output without knowing its internal model informatio...
| Authors | Xiaofei Hui, Qian Wu, Haoxuan Qu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 14 March 2026 |
| Last updated at source | 14 March 2026 |
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