LEARNING SEMI-SUPERVISED ANONYMIZED REPRESENTATIONS BY MUTUAL INFORMATION

被引：0

作者：

Feutry, C. ^{[1
]}

Piantanida, P. ^{[1
]}

Duhamel, P. ^{[1
]}

机构：

[1] Univ Paris Sud, CNRS, Cent Supelec, Lab Signaux & Syst, Gif, France

来源：

2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING | 2020年

关键词：

D O I：

10.1109/icassp40776.2020.9053379

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

This paper addresses the problem of removing from a set of data (here images) a given private information, while still allowing other utilities on the processed data. This is obtained by training concurrently a GAN-like discriminator and an autoencoder. The optimization of the resulting structure involves a novel surrogate of the misclassification probability of the information to remove. Several examples are given, demonstrating that a good level of privacy can be obtained on images at the cost of the introduction of very small artifacts.

引用

页码：3467 / 3471

页数：5

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