Privacy preservation for image data: A GAN‐based method

International Journal of Intelligent SystemsVolume 36, Issue 4 p. 1668-1685 RESEARCH ARTICLE Privacy preservation for image data: A GAN-based method Zhenfei Chen, Zhenfei Chen orcid.org/0000-0003-3493-5149 School of Computer Science, China University of Geosciences Wuhan, Wuhan, ChinaSearch for more papers by this authorTianqing Zhu, Corresponding Author Tianqing Zhu caohaitao@njnu.edu.cn orcid.org/0000-0003-3411-7947 School of Computer Science, China University of Geosciences Wuhan, Wuhan, China Correspondence Tianqing Zhu, School of Computer Science, China University of Geosciences Wuhan, Wuhan 430074, P.R. China Email: caohaitao@njnu.edu.cnSearch for more papers by this authorPing Xiong, Ping Xiong orcid.org/0000-0003-3289-3061 School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan, ChinaSearch for more papers by this authorChenguang Wang, Chenguang Wang orcid.org/0000-0002-7896-2360 School of Computer Science, China University of Geosciences Wuhan, Wuhan, ChinaSearch for more papers by this authorWei Ren, Wei Ren orcid.org/0000-0001-8590-1737 School of Computer Science, China University of Geosciences Wuhan, Wuhan, China Guangxi Key Laboratory of Cryptography and Information Security, Guilin, China Key Laboratory of Network Assessment Technology, Chinese Academy of Sciences, Beijing, ChinaSearch for more papers by this author Zhenfei Chen, Zhenfei Chen orcid.org/0000-0003-3493-5149 School of Computer Science, China University of Geosciences Wuhan, Wuhan, ChinaSearch for more papers by this authorTianqing Zhu, Corresponding Author Tianqing Zhu caohaitao@njnu.edu.cn orcid.org/0000-0003-3411-7947 School of Computer Science, China University of Geosciences Wuhan, Wuhan, China Correspondence Tianqing Zhu, School of Computer Science, China University of Geosciences Wuhan, Wuhan 430074, P.R. China Email: caohaitao@njnu.edu.cnSearch for more papers by this authorPing Xiong, Ping Xiong orcid.org/0000-0003-3289-3061 School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan, ChinaSearch for more papers by this authorChenguang Wang, Chenguang Wang orcid.org/0000-0002-7896-2360 School of Computer Science, China University of Geosciences Wuhan, Wuhan, ChinaSearch for more papers by this authorWei Ren, Wei Ren orcid.org/0000-0001-8590-1737 School of Computer Science, China University of Geosciences Wuhan, Wuhan, China Guangxi Key Laboratory of Cryptography and Information Security, Guilin, China Key Laboratory of Network Assessment Technology, Chinese Academy of Sciences, Beijing, ChinaSearch for more papers by this author First published: 05 January 2021 https://doi.org/10.1002/int.22356Citations: 3Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract The importance of protecting personal information, like, a person's address or health history, is well known and commonly discussed. However, images also contain sensitive information that can compromise a person's privacy or be used for nefarious purposes. To date, most methods for preserving privacy with images have relied on obfuscation techniques, such as pixelation, blurring, or masking parts of the image. However, new face-recognition technologies driven by deep learning are showing cracks in the old techniques. Moreover, faceless recognition is presenting a whole new set of challenges for image privacy. The core of these issues it is how to ensure privacy while still being able to see and use the image. Our solution is a model based on a generative adversarial network that protects identity information while preserving face features of the original image as much as possible. The premise is to generate a fake image of a face that shares all the same attributes as the original image, for example, a brown-eyed child smiling. With this strategy, the image remains useful, but no person or algorithm could determine the identity of the pictured individual. The framework consists of three parts: a detection module, an image creation module, and an image transformation module. The detection module extracts the attribute labels. The image creation module generates images of faces, and the image transformation module transforms the fake features to match the attributes in the original image. A comprehensive set of experiments shows the effectiveness of the proposed framework. Citing Literature Volume36, Issue4April 2021Pages 1668-1685 RelatedInformation