More than Meets the Eye: Understanding the Effect of Individual Objects on Perceived Visual Privacy

2025-09-16

AI Summary

Prior work treats image privacy as a holistic property, overlooking differential privacy risks associated with individual sensitive objects in multi-object images and their impact on user perception. Method: We propose an object-level visual privacy analysis framework, employing a mixed-methods approach—including qualitative interviews and quantitative experiments with 92 participants—to systematically investigate how capture context and object co-occurrence shape users’ privacy judgments. Contribution/Results: We find that contextual cues significantly modulate privacy risk assessments for individual objects, and users’ privacy perceptions of images containing multiple sensitive objects exhibit both situational dependency and object-level heterogeneity. Building on these findings, we develop a context- and co-occurrence-aware privacy perception model. This model provides theoretical grounding and empirical evidence for designing fine-grained, personalized privacy protection mechanisms tailored to social media platforms.

Technology Category

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

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Security and Privacy: Large-scale security measurements
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

User-generated content, such as photos, comprises the majority of online media content and drives engagement due to the human ability to process visual information quickly. Consequently, many online platforms are designed for sharing visual content, with billions of photos posted daily. However, photos often reveal more than they intended through visible and contextual cues, leading to privacy risks. Previous studies typically treat privacy as a property of the entire image, overlooking individual objects that may carry varying privacy risks and influence how users perceive it. We address this gap with a mixed-methods study (n = 92) to understand how users evaluate the privacy of images containing multiple sensitive objects. Our results reveal mental models and nuanced patterns that uncover how granular details, such as photo-capturing context and co-presence of other objects, affect privacy perceptions. These novel insights could enable personalized, context-aware privacy protection designs on social media and future technologies.

Problem

• Understanding individual objects' impact on perceived visual privacy
• Examining how multiple sensitive objects influence privacy evaluations
• Investigating granular details affecting privacy perceptions in images

Innovation

• Individual object privacy risk analysis
• Mixed-methods study revealing mental models
• Context-aware personalized privacy protection