Perceptual Indistinguishability-Net (PI-Net): Facial Image Obfuscation with Manipulable Semantics

Jia-Wei Chen, Li-Ju Chen, Chia-Mu Yu, Chun-Shien Lu

研究成果: Paper同行評審

21 引文 斯高帕斯(Scopus)

摘要

With the growing use of camera devices, the industry has many image datasets that provide more opportunities for collaboration between the machine learning community and industry. However, the sensitive information in the datasets discourages data owners from releasing these datasets. Despite recent research devoted to removing sensitive information from images, they provide neither meaningful privacy-utility trade-off nor provable privacy guarantees. In this study, with the consideration of the perceptual similarity, we propose perceptual indistinguishability (PI) as a formal privacy notion particularly for images. We also propose PI-Net, a privacy-preserving mechanism that achieves image obfuscation with PI guarantee. Our study shows that PI-Net achieves significantly better privacy utility trade-off through public image data.
原文American English
DOIs
出版狀態Published - 13 11月 2021
事件2021 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) -
持續時間: 19 6月 202125 6月 2021

Conference

Conference2021 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
期間19/06/2125/06/21

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