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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.01208 |
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| _version_ | 1866915342667743232 |
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| author | Ju, Tianjie Hua, Yi Fei, Hao Shao, Zhenyu Zheng, Yubin Zhao, Haodong Lee, Mong-Li Hsu, Wynne Zhang, Zhuosheng Liu, Gongshen |
| author_facet | Ju, Tianjie Hua, Yi Fei, Hao Shao, Zhenyu Zheng, Yubin Zhao, Haodong Lee, Mong-Li Hsu, Wynne Zhang, Zhuosheng Liu, Gongshen |
| contents | Multi-Modal Large Language Models (MLLMs) have exhibited remarkable performance on various vision-language tasks such as Visual Question Answering (VQA). Despite accumulating evidence of privacy concerns associated with task-relevant content, it remains unclear whether MLLMs inadvertently memorize private content that is entirely irrelevant to the training tasks. In this paper, we investigate how randomly generated task-irrelevant private content can become spuriously correlated with downstream objectives due to partial mini-batch training dynamics, thus causing inadvertent memorization. Concretely, we randomly generate task-irrelevant watermarks into VQA fine-tuning images at varying probabilities and propose a novel probing framework to determine whether MLLMs have inadvertently encoded such content. Our experiments reveal that MLLMs exhibit notably different training behaviors in partial mini-batch settings with task-irrelevant watermarks embedded. Furthermore, through layer-wise probing, we demonstrate that MLLMs trigger distinct representational patterns when encountering previously seen task-irrelevant knowledge, even if this knowledge does not influence their output during prompting. Our code is available at https://github.com/illusionhi/ProbingPrivacy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_01208 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Watch Out Your Album! On the Inadvertent Privacy Memorization in Multi-Modal Large Language Models Ju, Tianjie Hua, Yi Fei, Hao Shao, Zhenyu Zheng, Yubin Zhao, Haodong Lee, Mong-Li Hsu, Wynne Zhang, Zhuosheng Liu, Gongshen Computer Vision and Pattern Recognition Computation and Language Multi-Modal Large Language Models (MLLMs) have exhibited remarkable performance on various vision-language tasks such as Visual Question Answering (VQA). Despite accumulating evidence of privacy concerns associated with task-relevant content, it remains unclear whether MLLMs inadvertently memorize private content that is entirely irrelevant to the training tasks. In this paper, we investigate how randomly generated task-irrelevant private content can become spuriously correlated with downstream objectives due to partial mini-batch training dynamics, thus causing inadvertent memorization. Concretely, we randomly generate task-irrelevant watermarks into VQA fine-tuning images at varying probabilities and propose a novel probing framework to determine whether MLLMs have inadvertently encoded such content. Our experiments reveal that MLLMs exhibit notably different training behaviors in partial mini-batch settings with task-irrelevant watermarks embedded. Furthermore, through layer-wise probing, we demonstrate that MLLMs trigger distinct representational patterns when encountering previously seen task-irrelevant knowledge, even if this knowledge does not influence their output during prompting. Our code is available at https://github.com/illusionhi/ProbingPrivacy. |
| title | Watch Out Your Album! On the Inadvertent Privacy Memorization in Multi-Modal Large Language Models |
| topic | Computer Vision and Pattern Recognition Computation and Language |
| url | https://arxiv.org/abs/2503.01208 |