Examining Human Perception of Generative Content Replacement in Image Privacy Protection
Xu, Anran; Fang, Shitao; Yang, Huan; Hosio, Simo; Yatani, Koji (2024-05-11)
ACM
11.05.2024
Anran Xu, Shitao Fang, Huan Yang, Simo Hosio, and Koji Yatani. 2024. Examining Human Perception of Generative Content Replacement in Image Privacy Protection. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (CHI '24). Association for Computing Machinery, New York, NY, USA, Article 777, 1–16. https://doi.org/10.1145/3613904.3642103
https://rightsstatements.org/vocab/InC/1.0/
© 2024 Copyright held by the owner/author(s). This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in CHI '24: CHI Conference on Human Factors in Computing Systems, http://dx.doi.org/10.1145/3613904.3642103
https://rightsstatements.org/vocab/InC/1.0/
© 2024 Copyright held by the owner/author(s). This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in CHI '24: CHI Conference on Human Factors in Computing Systems, http://dx.doi.org/10.1145/3613904.3642103
https://rightsstatements.org/vocab/InC/1.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202410286473
https://urn.fi/URN:NBN:fi:oulu-202410286473
Tiivistelmä
Abstract
The richness of the information in photos can often threaten privacy, thus image editing methods are often employed for privacy protection. Existing image privacy protection techniques, like blurring, often struggle to maintain the balance between robust privacy protection and preserving image usability. To address this, we introduce a generative content replacement (GCR) method in image privacy protection, which seamlessly substitutes privacy-threatening contents with similar and realistic substitutes, using state-of-the-art generative techniques. Compared with four prevalent image protection methods, GCR consistently exhibited low detectability, making the detection of edits remarkably challenging. GCR also performed reasonably well in hindering the identification of specific content and managed to sustain the image’s narrative and visual harmony. This research serves as a pilot study and encourages further innovation on GCR and the development of tools that enable human-in-the-loop image privacy protection using approaches similar to GCR.
The richness of the information in photos can often threaten privacy, thus image editing methods are often employed for privacy protection. Existing image privacy protection techniques, like blurring, often struggle to maintain the balance between robust privacy protection and preserving image usability. To address this, we introduce a generative content replacement (GCR) method in image privacy protection, which seamlessly substitutes privacy-threatening contents with similar and realistic substitutes, using state-of-the-art generative techniques. Compared with four prevalent image protection methods, GCR consistently exhibited low detectability, making the detection of edits remarkably challenging. GCR also performed reasonably well in hindering the identification of specific content and managed to sustain the image’s narrative and visual harmony. This research serves as a pilot study and encourages further innovation on GCR and the development of tools that enable human-in-the-loop image privacy protection using approaches similar to GCR.
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