Natural Face Anonymization via Latent Space Layers Swapping

Machine learning is widely recognized as a key driver of technological progress. Artificial Intelligence (AI) applications that interact with humans require access to vast quantities of human image data. However, the use of large, real-world image datasets containing faces raises serious concerns ab...

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Bibliographic Details
Main Authors: BenSaid, Emna, Neji, Mohamed, Alimi, Adel M.
Format: Conference Proceeding
Language:English
Subjects:
Anonymization
Data privacy
Faces
Image segmentation
Information filtering
Latent Space
Layers Swapping
Machine learning
Pravicy
Privacy
StyleGan
Visualization
Online Access:Request full text
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Summary:Machine learning is widely recognized as a key driver of technological progress. Artificial Intelligence (AI) applications that interact with humans require access to vast quantities of human image data. However, the use of large, real-world image datasets containing faces raises serious concerns about privacy. In this paper, we examine the issue of anonymizing image datasets that include faces. Our approach modifies the facial features that contribute to personal identification, resulting in an altered facial appearance that conceals the person's identity. This is achieved without compromising other visual features such as posture, facial expression, and hairstyle while maintaining a natural-looking appearance. Finally, Our method offers adjustable levels of privacy, computationally efficient, and has demonstrated superior performance compared to existing methods.
ISSN:2642-7389
DOI:10.1109/ISCC58397.2023.10218009