Imago Obscura: An Image Privacy AI Co-pilot to Enable Identification and Mitigation of Risks
May 27, 2025 Β· Declared Dead Β· π ACM Symposium on User Interface Software and Technology
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Authors
Kyzyl Monteiro, Yuchen Wu, Sauvik Das
arXiv ID
2505.20916
Category
cs.HC: Human-Computer Interaction
Citations
5
Venue
ACM Symposium on User Interface Software and Technology
Last Checked
4 months ago
Abstract
Users often struggle to navigate the privacy / publicity boundary in sharing images online: they may lack awareness of image privacy risks and/or the ability to apply effective mitigation strategies. To address this challenge, we introduce and evaluate Imago Obscura, an AI-powered, image-editing copilot that enables users to identify and mitigate privacy risks with images they intend to share. Driven by design requirements from a formative user study with 7 image-editing experts, Imago Obscura enables users to articulate their image-sharing intent and privacy concerns. The system uses these inputs to surface contextually pertinent privacy risks, and then recommends and facilitates application of a suite of obfuscation techniques found to be effective in prior literature -- e.g., inpainting, blurring, and generative content replacement. We evaluated Imago Obscura with 15 end-users in a lab study and found that it greatly improved users' awareness of image privacy risks and their ability to address those risks, allowing them to make more informed sharing decisions.
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