DP^2-VL: Private Photo Dataset Protection by Data Poisoning for Vision-Language Models

Hongyi Miao, Jun Jia, Xincheng Wang, Qianli Ma, Wei Sun, Wangqiu Zhou, Dandan Zhu, Yewen Cao, Zhi Liu, Guangtao Zhai

Abstract

Recent advances in visual-language alignment have endowed vision-language models (VLMs) with fine-grained image understanding capabilities. However, this progress also introduces new privacy risks. This paper first proposes a novel privacy threat model named identity-affiliation learning: an attacker fine-tunes a VLM using only a few private photos of a target individual, thereby embedding associations between the target facial identity and their private property and social relationships into th

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Cite This Resource

@article{llmsec202600160,
  title = {DP^2-VL: Private Photo Dataset Protection by Data Poisoning for Vision-Language Models},
  author = {Hongyi Miao and Jun Jia and Xincheng Wang and Qianli Ma and Wei Sun and Wangqiu Zhou and Dandan Zhu and Yewen Cao and Zhi Liu and Guangtao Zhai},
  year = {2026},
  url = {https://arxiv.org/abs/2603.23925},
}