3PFS: protecting pedestrian privacy through face swapping
File(s)
Accepted version
Author(s)
Zhang, Zixian
Zhang, Xingchen
Demiris, Yiannis
Type
Journal Article
Abstract
In the era of artificial intelligence, privacy has become a paramount concern, especially within intelligent transportation systems (ITS) where pedestrians are frequently captured by vehicle-mounted cameras for deep learning model training. To address this, we introduce 3PFS, a novel method designed to protect pedestrian privacy via face swapping while preserving the utility of processed images. Our method consists of a pedestrian detector, a face detector, a pre-processing module, a source face selection algorithm, and a face swapping algorithm. After detecting pedestrians and their corresponding faces, the pre-processing module enhances image quality. Our unique source face selection algorithm then chooses an appropriate face from our source face library, which is subsequently swapped with the target face using a face swapping algorithm. Notably, with the combination of a pedestrian tracking algorithm, our 3PFS is well-suited for video anonymization. Additionally, we propose a comprehensive evaluation strategy to evaluate the performance of pedestrian anonymization methods. We validate the effectiveness of 3PFS through extensive experiments on a dataset we created based on the publicly available JAAD dataset and on videos captured using our robotic wheelchair.
Date Issued
2024-11-01
Date Acceptance
2024-06-16
Citation
IEEE Transactions on Intelligent Transportation Systems, 2024, 25 (11), pp.16845-16854
ISSN
1524-9050
Publisher
Institute of Electrical and Electronics Engineers
Start Page
16845
End Page
16854
Journal / Book Title
IEEE Transactions on Intelligent Transportation Systems
Volume
25
Issue
11
Copyright Statement
Copyright © 2024 IEEE. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Identifier
https://ieeexplore.ieee.org/abstract/document/10682960
Publication Status
Published
Date Publish Online
2024-09-18