Learning Sensitive Images Using Generative Models

Sen Ching Samson Cheung; Herb Wildfeuer; Mehdi Nikkhah; Xiaoqing Zhu; Wai Tian Tan

doi:10.1109/ICIP.2018.8451239

Learning Sensitive Images Using Generative Models

Sen Ching Samson Cheung, Herb Wildfeuer, Mehdi Nikkhah, Xiaoqing Zhu, Wai Tian Tan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

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.

Original language	English
Title of host publication	2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings
Pages	4128-4132
Number of pages	5
ISBN (Electronic)	9781479970612
DOIs	https://doi.org/10.1109/ICIP.2018.8451239
State	Published - Aug 29 2018
Event	25th IEEE International Conference on Image Processing, ICIP 2018 - Athens, Greece Duration: Oct 7 2018 → Oct 10 2018

Publication series

Name	Proceedings - International Conference on Image Processing, ICIP
ISSN (Print)	1522-4880

Conference

Conference	25th IEEE International Conference on Image Processing, ICIP 2018
Country/Territory	Greece
City	Athens
Period	10/7/18 → 10/10/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

Face processing
Generative adversarial network
Privacy preserving classification

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition
Signal Processing

Access to Document

10.1109/ICIP.2018.8451239

Cite this

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title = "Learning Sensitive Images Using Generative Models",

abstract = "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.",

keywords = "Face processing, Generative adversarial network, Privacy preserving classification",

author = "Cheung, {Sen Ching Samson} and Herb Wildfeuer and Mehdi Nikkhah and Xiaoqing Zhu and Tan, {Wai Tian}",

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doi = "10.1109/ICIP.2018.8451239",

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Cheung, SCS, Wildfeuer, H, Nikkhah, M, Zhu, X & Tan, WT 2018, Learning Sensitive Images Using Generative Models. in 2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings., 8451239, Proceedings - International Conference on Image Processing, ICIP, pp. 4128-4132, 25th IEEE International Conference on Image Processing, ICIP 2018, Athens, Greece, 10/7/18. https://doi.org/10.1109/ICIP.2018.8451239

Learning Sensitive Images Using Generative Models. / Cheung, Sen Ching Samson; Wildfeuer, Herb; Nikkhah, Mehdi et al.
2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings. 2018. p. 4128-4132 8451239 (Proceedings - International Conference on Image Processing, ICIP).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Learning Sensitive Images Using Generative Models

AU - Cheung, Sen Ching Samson

AU - Wildfeuer, Herb

AU - Nikkhah, Mehdi

AU - Zhu, Xiaoqing

AU - Tan, Wai Tian

PY - 2018/8/29

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AB - 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.

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Learning Sensitive Images Using Generative Models

Abstract

Publication series

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Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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