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• W3203194053 abstract "The popularization of intelligent devices including smartphones and surveillance cameras results in more serious privacy issues. De-identification is regarded as an effective tool for visual privacy protection with the process of concealing or replacing identity information. Most of the existing de-identification methods suffer from some limitations since they mainly focus on the protection process and are usually non-reversible. In this paper, we propose a personalized and invertible de-identification method based on the deep generative model, where the main idea is introducing a user-specific password and an adjustable parameter to control the direction and degree of identity variation. Extensive experiments demonstrate the effectiveness and generalization of our proposed framework for both face de-identification and recovery." @default.
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• W3203194053 date "2021-10-01" @default.
• W3203194053 modified "2023-10-03" @default.
• W3203194053 title "Personalized and Invertible Face De-identification by Disentangled Identity Information Manipulation" @default.
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• W3203194053 doi "https://doi.org/10.1109/iccv48922.2021.00332" @default.
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