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Delegate-based Utility Preserving Synthesis for Pedestrian Image Anonymization

Published: 10 October 2022 Publication History

Abstract

The 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.

Supplementary Material

MP4 File (MM22-fp2022.mp4)
The presentation video of DUPS for pedestrian image anonymization

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Cited By

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  • (2025)Visual Content Privacy Protection: A SurveyACM Computing Surveys10.1145/370850157:5(1-36)Online publication date: 24-Jan-2025
  • (2024)CODER: Protecting Privacy in Image Retrieval With Differential PrivacyIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2024.337653221:6(5420-5430)Online publication date: Nov-2024

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cover image ACM Conferences
MM '22: Proceedings of the 30th ACM International Conference on Multimedia
October 2022
7537 pages
ISBN:9781450392037
DOI:10.1145/3503161
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Published: 10 October 2022

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Author Tags

  1. pedestrian image
  2. privacy protection
  3. reusable
  4. synthesis

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View all
  • (2025)Visual Content Privacy Protection: A SurveyACM Computing Surveys10.1145/370850157:5(1-36)Online publication date: 24-Jan-2025
  • (2024)CODER: Protecting Privacy in Image Retrieval With Differential PrivacyIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2024.337653221:6(5420-5430)Online publication date: Nov-2024

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