Imago Obscura: An Image Privacy AI Co-pilot to Enable Identification and Mitigation of Risks
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909624962121728 |
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| author | Monteiro, Kyzyl Wu, Yuchen Das, Sauvik |
| author_facet | Monteiro, Kyzyl Wu, Yuchen Das, Sauvik |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_20916 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Imago Obscura: An Image Privacy AI Co-pilot to Enable Identification and Mitigation of Risks Monteiro, Kyzyl Wu, Yuchen Das, Sauvik Human-Computer Interaction H.5.2; K.4.1 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. |
| title | Imago Obscura: An Image Privacy AI Co-pilot to Enable Identification and Mitigation of Risks |
| topic | Human-Computer Interaction H.5.2; K.4.1 |
| url | https://arxiv.org/abs/2505.20916 |