# The impact of abstract and object tags on image privacy classification

arXiv: https://arxiv.org/abs/2510.07976 · HTML: https://mlsanity.com/paper/2510.07976/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 27 references that are in the map.
- First posted on arXiv 9 Oct 2025.

## Builds on (most cited first, 14 of 14)

- [Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classification](https://mlsanity.com/paper/2211.11158/) (2022)
- [Towards a Visual Privacy Advisor: Understanding and Predicting Privacy Risks in Images](https://mlsanity.com/paper/1703.10660/) (2017)
- [Tag2Text: Guiding Vision-Language Model via Image Tagging](https://mlsanity.com/paper/2303.05657/) (2023)
- [Quantifying the visual concreteness of words and topics in multimodal datasets](https://mlsanity.com/paper/1804.06786/) (2018)
- [IDEA: Increasing Text Diversity via Online Multi-Label Recognition for Vision-Language Pre-training](https://mlsanity.com/paper/2207.05333/) (2022)
- [Image Privacy Prediction Using Deep Neural Networks](https://mlsanity.com/paper/1903.03695/) (2019)
- [Privacy-Aware Visual Language Models](https://mlsanity.com/paper/2405.17423/) (2024)
- [Content-based Graph Privacy Advisor](https://mlsanity.com/paper/2210.11169/) (2022)
- [Human-interpretable and deep features for image privacy classification](https://mlsanity.com/paper/2310.19582/) (2023)
- [Image-guided topic modeling for interpretable privacy classification](https://mlsanity.com/paper/2409.18674/) (2024)
- [PEAK: Explainable Privacy Assistant through Automated Knowledge Extraction](https://mlsanity.com/paper/2301.02079/) (2023)
- [Identifying Privacy Personas](https://mlsanity.com/paper/2410.14023/) (2024)
- [Learning Privacy from Visual Entities](https://mlsanity.com/paper/2503.12464/) (2025)
- [Impact of Contextual Factors on Snapchat Public Sharing](https://mlsanity.com/paper/1903.07033/) (2019)

## Abstract (arXiv)

Object tags denote concrete entities and are central to many computer vision tasks, whereas abstract tags capture higher-level information, which is relevant for tasks that require a contextual, potentially subjective scene understanding. Object and abstract tags extracted from images also facilitate interpretability. In this paper, we explore which type of tags is more suitable for the context-dependent and inherently subjective task of image privacy. While object tags are generally used for privacy classification, we show that abstract tags are more effective when the tag budget is limited. Conversely, when a larger number of tags per image is available, object-related information is as useful. We believe that these findings will guide future research in developing more accurate image privacy classifiers, informed by the role of tag types and quantity.