Predicting and explaining the private information contained in an image in human-understandable terms is a complex and contextual task. This task is challenging even for large language models. To facilitate the understanding of privacy decisions, we propose to predict image privacy based on a set of natural language content descriptors. These content descriptors are associated with privacy scores that reflect how people perceive image content. We generate descriptors with our novel Image-guided Topic Modeling (ITM) approach. ITM leverages, via multimodality alignment, both vision information and image textual descriptions from a vision language model. We use the ITM-generated descriptors to learn a privacy predictor, Priv$\times$ITM, whose decisions are interpretable by design. Our Priv$\times$ITM classifier outperforms the reference interpretable method by 5 percentage points in accuracy and performs comparably to the current non-interpretable state-of-the-art model.

翻译：以人类可理解的术语预测和解释图像中包含的隐私信息是一项复杂且依赖上下文的任务。即使对于大型语言模型而言，这项任务也具有挑战性。为了促进对隐私决策的理解，我们提出基于一组自然语言内容描述符来预测图像隐私。这些内容描述符与反映人们对图像内容感知的隐私评分相关联。我们通过新颖的图像引导主题建模方法生成描述符。该方法通过多模态对齐，同时利用视觉信息以及来自视觉语言模型的图像文本描述。我们使用ITM生成的描述符来学习一个隐私预测器Priv$\times$ITM，其决策过程在设计上即是可解释的。我们的Priv$\times$ITM分类器在准确率上比参考的可解释方法高出5个百分点，并与当前不可解释的最先进模型性能相当。