research-article

On Generating Identifiable Virtual Faces

Authors:

Alex KotAuthors Info & Claims

MM '22: Proceedings of the 30th ACM International Conference on Multimedia

Pages 1465 - 1473

https://doi.org/10.1145/3503161.3548110

Published: 10 October 2022 Publication History

Abstract

Face anonymization with generative models have become increasingly prevalent since they sanitize private information by generating virtual face images, ensuring both privacy and image utility. Such virtual face images are usually not identifiable after the removal or protection of the original identity. In this paper, we formalize and tackle the problem of generating identifiable virtual face images. Our virtual face images are visually different from the original ones for privacy protection. In addition, they are bound with new virtual identities, which can be directly used for face recognition. We propose an Identifiable Virtual Face Generator (IVFG) to generate the virtual face images. The IVFG projects the latent vectors of the original face images into virtual ones according to a user specific key, based on which the virtual face images are generated. To make the virtual face images identifiable, we propose a multi-task learning objective as well as a triplet styled training strategy to learn the IVFG. We evaluate the performance of our virtual face images using different face recognizers on diffident face image datasets, all of which demonstrate the effectiveness of the IVFG for generate identifiable virtual face images.

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Cited By

Wang TZhang YQi SZhao RXia ZWeng J(2024)Security and Privacy on Generative Data in AIGC: A SurveyACM Computing Surveys10.1145/370362657:4(1-34)Online publication date: 10-Dec-2024
https://dl.acm.org/doi/10.1145/3703626
Li GLi SQian ZZhang XCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Cover-separable Fixed Neural Network Steganography via Deep Generative ModelsProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680824(10238-10247)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680824
Wang TZhang YXiao XYuan LXia ZWeng JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Make Privacy Renewable! Generating Privacy-Preserving Faces Supporting Cancelable Biometric RecognitionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680704(10268-10276)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680704
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Index Terms

On Generating Identifiable Virtual Faces
1. Security and privacy
  1. Security services
    1. Authentication
      1. Biometrics

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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

General Chairs:
João Magalhães
NOVA University of Lisbon, Portugal
,
Alberto del Bimbo
University of Florence, Italy
,
Shin'ichi Satoh
National Institute of Informatics, Japan
,
Nicu Sebe
University of Trento, Italy
,
Program Chairs:
Xavier Alameda-Pineda
Inria, Grenoble, France
,
Qin Jin
Renmin University of China, China
,
Vincent Oria
New Jersey Institute of Technology, USA
,
Laura Toni
University College London, UK

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Published: 10 October 2022

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National Natural Science Foundation of China

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MM '22

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SIGMM

MM '22: The 30th ACM International Conference on Multimedia

October 10 - 14, 2022

Lisboa, Portugal

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

Wang TZhang YQi SZhao RXia ZWeng J(2024)Security and Privacy on Generative Data in AIGC: A SurveyACM Computing Surveys10.1145/370362657:4(1-34)Online publication date: 10-Dec-2024
https://dl.acm.org/doi/10.1145/3703626
Li GLi SQian ZZhang XCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Cover-separable Fixed Neural Network Steganography via Deep Generative ModelsProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680824(10238-10247)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680824
Wang TZhang YXiao XYuan LXia ZWeng JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Make Privacy Renewable! Generating Privacy-Preserving Faces Supporting Cancelable Biometric RecognitionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680704(10268-10276)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680704
Zhang YJi JWen WZhu YXia ZWeng J(2024)Understanding Visual Privacy Protection: A Generalized Framework With an Instance on Facial PrivacyIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.338957219(5046-5059)Online publication date: 2024
https://doi.org/10.1109/TIFS.2024.3389572
Yuan LChen WPu XZhang YLi HZhang YGao XEbrahimi T(2024)PRO-Face C: Privacy-Preserving Recognition of Obfuscated Face via Feature CompensationIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.338897619(4930-4944)Online publication date: 2024
https://doi.org/10.1109/TIFS.2024.3388976
Yuan LLiang KPu XZhang YLeng JWu TWang NGao X(2024)Invertible Image Obfuscation for Facial Privacy Protection via Secure FlowIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.334480934:7(6077-6091)Online publication date: Jul-2024
https://doi.org/10.1109/TCSVT.2023.3344809
Jiang DAhmad JSuo ZAlsulami MGhadi YBoulila W(2024)ML-FAS: Multi-Level Face Anonymization Scheme and Its Application to E-Commerce SystemsIEEE Transactions on Consumer Electronics10.1109/TCE.2024.341110270:3(5090-5100)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/TCE.2024.3411102
Kuang ZYang XShen YHu CYu J(2024)Facial Identity Anonymization via Intrinsic and Extrinsic Attention Distraction2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.01179(12406-12415)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.01179
Thakral KPrasad SAswani SVatsa MSingh R(2024)ToonerGAN: Reinforcing GANs for Obfuscating Automated Facial Indexing2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.01034(10875-10884)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.01034
Zhu MHe PZhang YLi JQiu Y(2024)Against linkage: A novel generative face anonymization framework with style diversificationIET Image Processing10.1049/ipr2.13237Online publication date: 30-Sep-2024
https://doi.org/10.1049/ipr2.13237
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