# Imago Obscura: An Image Privacy AI Co-pilot to Enable Identification and Mitigation of Risks

arXiv: https://arxiv.org/abs/2505.20916 · HTML: https://mlsanity.com/paper/2505.20916/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2025.

## Built on by (most cited first, 3 of 3)

- [Privacy in Human-AI Romantic Relationships: Concerns, Boundaries, and Agency](https://mlsanity.com/paper/2601.16824/) (2026)
- [From Fragmentation to Integration: Exploring the Design Space of AI Agents for Human-as-the-Unit Privacy Management](https://mlsanity.com/paper/2602.05016/) (2026)
- [AuraMask: An Extensible Pipeline for Developing Aesthetic Anti-Facial Recognition Image Filters](https://mlsanity.com/paper/2605.12937/) (2026)

## Abstract (arXiv)

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.