The impact of abstract and object tags on image privacy classification

2025-10-09

AI Summary

This study investigates how label semantic granularity—abstract versus object-level labels—affects image privacy classification performance under both label-scarce and label-sufficient conditions. We propose a novel paradigm asserting that “label semantic granularity must align with the subjective nature of the privacy classification task,” and develop a framework integrating visual label extraction with context-aware scene understanding to systematically compare the two label types. Experimental results demonstrate that under label scarcity, abstract labels significantly outperform object-level labels (+8.2% accuracy), as their high-level, subjective semantics better capture human privacy judgments. In contrast, when abundant labels are available, both label types achieve comparable performance. This finding challenges the conventional reliance on fine-grained object-level annotations and establishes an efficient, scalable labeling strategy for low-resource privacy recognition—offering both theoretical insight into semantic alignment in privacy modeling and practical guidance for real-world deployment.

Technology Category

• Machine Learning: Privacy
• Computer Vision: Bias, Fairness & Privacy
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

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.

Problem

• Comparing abstract vs object tags for image privacy classification
• Determining optimal tag type under limited annotation budgets
• Evaluating tag quantity impact on privacy classification accuracy

Innovation

• Abstract tags outperform object tags with limited tag budget
• Object tags match abstract tags when many tags available
• Tag type and quantity guide privacy classifier development