GANonymizer: Image Anonymization Method Integrating Object Detection and Generative Adversarial Network

Tomoki Tanimura, Makoto Kawano, Takuro Yonezawa, Jin Nakazawa

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Sharing and analyzing image data from ubiquitous urban cameras must enable us to understand and predict various contexts of the city. Meanwhile, since such image data always contains privacy data such as people and cars, we cannot easily share and analyze the data through the Internet for the viewpoint of privacy protection. As a result, most of urban image data are only kept/shared within the camera owners, or even discarded to reduce risks of privacy data leakage. To solve the privacy problem and accelerate sharing of urban image data, we propose GANonymizer that automatically detects and removes objects related to privacy from the urban images. GANonymizer combines two neural networks: (1) a network which detects objects related to privacy such as persons and cars in an input image using object detection network and (2) a network that removes the detected objects naturally as though they are not exist originally. Through our experiment of applying GANonymizer to urban video images, we confirmed that GANonymizer partially achieved natural removal of objects related to privacy.

Original languageEnglish
Title of host publicationEAI/Springer Innovations in Communication and Computing
PublisherSpringer Science and Business Media Deutschland GmbH
Pages109-121
Number of pages13
DOIs
Publication statusPublished - 2020

Publication series

NameEAI/Springer Innovations in Communication and Computing
ISSN (Print)2522-8595
ISSN (Electronic)2522-8609

Keywords

  • DNN
  • Privacy protection
  • Urban image anonymization

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Electrical and Electronic Engineering
  • Health Informatics

Fingerprint Dive into the research topics of 'GANonymizer: Image Anonymization Method Integrating Object Detection and Generative Adversarial Network'. Together they form a unique fingerprint.

Cite this