Paper Info
Title
Delegate-based Utility Preserving Synthesis for Pedestrian Image Anonymization
Abstract
ABSTRACTThe rapidly growing application of pedestrian images has aroused wide concern on visual privacy protection because personal information is under the risk of privacy disclosure. Anonymization is regarded as an effective solution by identity obfuscation. Most recent methods focus on face, but it is not enough when the presence of human body carries lots of identifiable information. This paper presents a new delegate-based utility preserving synthesis (DUPS) approach for pedestrian image anonymization. This is challenging because one may expect that the anonymized image can still be useful in various computer vision tasks. We model DUPS as an adaptive translation process from source to target. To provide a comprehensive identity protection, we first perform anonymous delegate sampling based on image-level differential privacy. To synthesize anonymous images, we then introduce an adaptive translation network and optimize it with a multi-task loss function. Our approach is theoretically sound and can generate diverse results by preserving data utility. The experiments on multiple datasets show that DUPS can not only achieve superior anonymization performance against deep pedestrian recognizers, but also can obtain a better tradeoff between privacy protection and utility preservation compared with state-of-the-art methods.
Year
DOI
Venue
2022
10.1145/3503161.3548235
International Multimedia Conference
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Zhenzhong Kuang16211.86
Longbin Teng200.34
Zhou Yu327839.88
Jun Yu42597105.69
Jianping Fan52677192.33
Mingliang Xu637254.07