用视觉语言模型识别图像中的法律定义个人数据，结果可解释。
PrivLEX: Detecting legal concepts in images through Vision-Language Models
- 零样本检测图像中法律定义的个人数据概念
- 无需标注概念即可实现可解释分类
- 适合隐私合规与AI伦理研究者使用
我们提出PrivLEX，一种新型图像隐私分类器，其决策基于法律定义的个人数据概念。PrivLEX是首个与法律概念对齐的可解释隐私分类器，利用视觉语言模型（VLMs）的识别能力。它通过零样本VLM概念检测，在无需训练时提供显式概念标签的情况下，借助无标签的概念瓶颈模型实现可解释分类。我们展示了PrivLEX识别图像中个人数据概念的能力，并进一步分析了这些概念在图像隐私数据集的人类标注者中的感知敏感性。
原文摘要 · Abstract (English)
We present PrivLEX, a novel image privacy classifier that grounds its decisions in legally defined personal data concepts. PrivLEX is the first interpretable privacy classifier aligned with legal concepts that leverages the recognition capabilities of Vision-Language Models (VLMs). PrivLEX relies on zero-shot VLM concept detection to provide interpretable classification through a label-free Concept Bottleneck Model, without requiring explicit concept labels during training. We demonstrate PrivLEX's ability to identify personal data concepts that are present in images. We further analyse the sensitivity of such concepts as perceived by human annotators of image privacy datasets.
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