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 proceedingConference contributionpeer-review

5 Scopus citations


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 languageEnglish
Title of host publication2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings
Number of pages5
ISBN (Electronic)9781479970612
StatePublished - Aug 29 2018
Event25th IEEE International Conference on Image Processing, ICIP 2018 - Athens, Greece
Duration: Oct 7 2018Oct 10 2018

Publication series

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


Conference25th IEEE International Conference on Image Processing, ICIP 2018

Bibliographical note

Publisher Copyright:
© 2018 IEEE.


  • Face processing
  • Generative adversarial network
  • Privacy preserving classification

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing


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