{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T02:15:56Z","timestamp":1777601756525,"version":"3.51.4"},"reference-count":51,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2021,11,9]],"date-time":"2021-11-09T00:00:00Z","timestamp":1636416000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61902262"],"award-info":[{"award-number":["61902262"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Science and Technology Project of Sichuan Province","award":["2019YFG0398"],"award-info":[{"award-number":["2019YFG0398"]}]},{"name":"Key Science and Technology Project of Sichuan Province","award":["2018GZDZX0011"],"award-info":[{"award-number":["2018GZDZX0011"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,3,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In recent years, deep neural networks have been extensively applied in various fields, and face recognition is one of the most important applications. Artificial intelligence has reached or even surpassed human capabilities in many fields. However, while artificial intelligence application provides convenience to the human lives, it also leads to the risk of privacy leaking. At present, the privacy protection technology for human faces has received extensive attention. Research goals of face privacy protection technology mainly include providing face anonymization and data availability protection. Existing methods usually have insufficient anonymity and they are not easy to control the degree of image distortion, which makes it difficult to achieve the purpose of privacy protection. Moreover, they do not explicitly perform diversity preservation of attributes such as emotions, expressions and ethnicities, so they cannot perform data analysis tasks on non-identity attributes. This paper proposes a diverse privacy face image generation algorithm based on machine learning, called DIVFGEN. This algorithm comprehensively considers image distortion, identity mapping distance loss and emotion classification loss; transforms the privacy protection target into the problem of generating adversarial examples based on the recognition model; and uses an adaptive optimization algorithm to generate anonymity and diversity of privacy images. The experimental results show that on the Cohn-Kanade+ dataset, our algorithm can reduce the probability of facial recognition by the neural network when it accurately classifies sentiment, from 98.6% to 4.8%.<\/jats:p>","DOI":"10.1093\/comjnl\/bxab176","type":"journal-article","created":{"date-parts":[[2021,11,9]],"date-time":"2021-11-09T20:14:55Z","timestamp":1636488895000},"page":"540-553","source":"Crossref","is-referenced-by-count":5,"title":["A Novel Generation Method for Diverse Privacy Image Based on Machine Learning"],"prefix":"10.1093","volume":"66","author":[{"given":"Weina","family":"Niu","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering , Institute for Cyber Security, University of Electronic Science and Technology of China, no. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China"}]},{"given":"Yuheng","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering , Institute for Cyber Security, University of Electronic Science and Technology of China, no. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China"}]},{"given":"Kangyi","family":"Ding","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering , Institute for Cyber Security, University of Electronic Science and Technology of China, no. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China"}]},{"given":"Xiaosong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering , Institute for Cyber Security, University of Electronic Science and Technology of China, no. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China"}]},{"given":"Yanping","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering , Institute for Cyber Security, University of Electronic Science and Technology of China, no. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China"}]},{"given":"Beibei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering , Sichuan University, no. 24 South Section 1, Yihuan Road, Chengdu 610065, China"}]}],"member":"286","published-online":{"date-parts":[[2021,11,9]]},"reference":[{"key":"2023031708554648200_","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1007\/978-3-319-50011-9_26","volume-title":"International Conference on Information and Communications Security","author":"Wong","year":"2016"},{"key":"2023031708554648200_","doi-asserted-by":"crossref","first-page":"1528","DOI":"10.1145\/2976749.2978392","volume-title":"Proceedings of the 2016 acm sigsac conference on computer and communications security","author":"Sharif","year":"2016"},{"key":"2023031708554648200_","article-title":"Privacy leakage in mobile computing: tools, methods, and characteristics","author":"Haris","year":"2014"},{"key":"2023031708554648200_","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1109\/TKDE.2005.32","article-title":"Preserving privacy by de-identifying face images","volume":"17","author":"Newton","year":"2005","journal-title":"IEEE Trans. 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