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• W3160710820 abstract "We live in an era of privacy concerns, motivating a large research effort in face de-identification. As in other fields, we are observing a general movement from hand-crafted to deep learning methods, mainly involving generative models. Although these methods produce more natural de-identified images or videos, we claim that the mere evaluation of the de-identification is not sufficient, especially when it comes to processing the images/videos further. In this note, we take into account the issue of preserving privacy, facial expressions, and photo-reality simultaneously, proposing a general testing framework. The quantitative evaluation is applied to four open-source tools, producing a baseline for future de-identification methods." @default.
• W3160710820 created "2021-05-24" @default.
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• W3160710820 date "2021-01-10" @default.
• W3160710820 modified "2023-10-17" @default.
• W3160710820 title "A Quantitative Evaluation Framework of Video De-Identification Methods" @default.
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• W3160710820 doi "https://doi.org/10.1109/icpr48806.2021.9412186" @default.
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