"I am not the primary focus" - Understanding the Perspectives of Bystanders in Photos Shared Online
Yuqi Niu, Nicole Meng-Schneider, Weidong Qiu, Nadin Kokciyan
Abstract
When taking photos in a crowd, unintended individuals, such as bystanders, are often captured alongside the main subject(s). In an effort to protect bystanders' privacy, existing methods have been developed to automatically detect bystanders. However, inconsistent definitions of who qualifies as a bystander limit their effectiveness. To better understand bystanders' perceptions, we conducted an online survey with 486 participants, analyzing their responses to 864 image-based scenarios and their comfort with sharing these images online. Our results revealed no significant correlation between comfort with public photo sharing and bystander status. We identified limitations in current bystander detection methodologies, as they often fail to recognize bystanders who are not clearly in the background, hence missing individuals with privacy concerns. Moreover, comfort with public sharing varied significantly depending on the image context. Our findings highlight the importance of considering the context of captured images to address privacy concerns in image sharing.
Ask about this paper
Ask your agent about it.
Lune has read the top-tier papers around this one, so every answer names the papers it rests on.
Your agent calls
Lunesearch_papers
Free to start. No credit card required.
Terminal
Install the CLIlune papers get e4a7c5a5-e93d-440a-b7ce-2c931cdd48b7Cited by top-tier papers2
- Mind the Gap: Mapping Wearer-Bystander Privacy Tensions and Context-Adaptive Pathways for Camera GlassesXueyang Wang, Kewen Peng, Xin Yi, Hewu LiCHI 2026 · 2 citations
- Do Vision-Language Models Respect Contextual Integrity in Location Disclosure?Ruixin Yang, Ethan Mendes, Arthur Wang, James Hays et al.ICLR 2026 · 1 citation
Related papers
- Automatically Detecting Bystanders in Photos to Reduce Privacy RisksRakibul Hasan, David J. Crandall, Mario Fritz, Apu KapadiaS&P 2020 · 67 citations
- Everyone's Privacy Matters! An Analysis of Privacy Leakage from Real-World Facial Images on Twitter and Associated User BehaviorsYuqi Niu, Weidong Qiu, Peng Tang, Lifan Wang et al.CSCW 2025 · 3 citations
- "If sighted people know, I should be able to know: " Privacy Perceptions of Bystanders with Visual Impairments around Camera-based TechnologyYuhang Zhao, Yaxing Yao, Jiaru Fu, Nihan ZhouUSENIX Security 2023
- "I am uncomfortable sharing what I can't see": Privacy Concerns of the Visually Impaired with Camera Based Assistive ApplicationsTaslima Akter, Bryan Dosono, Tousif Ahmed, Apu Kapadia et al.USENIX Security 2020
- Characterizing and Detecting Non-Consensual Photo Sharing on Social NetworksTengfei Zheng, Tongqing Zhou, Qiang Liu, Kui Wu et al.CCS 2022 · 6 citations
