LEARNING SENSITIVE IMAGES USING GENERATIVE MODELS

被引：0

作者：

Cheung, Sen-Ching Samson ^{[1
,3
]}

Wildfeuer, Herb ^{[2
]}

Nikkhah, Mehdi ^{[2
]}

Zhu, Xiaoqing ^{[2
]}

Tan, Wai-tian ^{[2
]}

机构：

[1] Univ Kentucky, Dept ECE, Lexington, KY 40506 USA

[2] Cisco Syst Inc, Innovat Labs, San Jose, CA USA

[3] Cisco Syst, San Jose, CA USA

来源：

2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | 2018年

关键词：

privacy preserving classification; generative adversarial network; face processing;

D O I：

暂无

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

The sheer amount of personal data being transmitted to cloud services and the ubiquity of cellphones cameras and various sensors, have provoked a privacy concern among many people. On the other hand, the recent phenomenal growth of deep learning that brings advancements in almost every aspect of human life is heavily dependent on the access to data, including sensitive images, medical records, etc. Therefore, there is a need for a mechanism that transforms sensitive data in such a way as to preserves the privacy of individuals, yet still be useful for deep learning algorithms. This paper proposes the use of Generative Adversarial Networks (GANs) as one such mechanism, and through experimental results, shows its efficacy.

引用

页码：4128 / 4132

页数：5

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