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Anonym-Recognizer: Relationship-preserving Face Anonymization and Recognition

Published: 10 October 2022 Publication History

Abstract

With the widespread application of big data technology, we are exposed to more and more video monitoring. To prevent serious social problems caused by face data leakage, face anonymization has become an important kind of method to protect face privacy. The face anonymization mentioned in this paper refers to the anonymization generation of the visual appearance in face images. Existing face anonymization methods mainly focus on removing identity information. However, in the scenario of face recognition technology that needs to protect privacy, existing face anonymization technology makes anonymized faces that can no longer be used for face recognition, limiting the application scope of face anonymization. Therefore, when using face anonymization, it is equally important to ensure that the anonymized face images can still be used for downstream tasks such as face recognition. To this end, we propose Anonym-Recognizer, a relationship-preserving face anonymization and recognition method. Our method uses relationship cyphertext which can be any binary identity number representing the identity of the image owner and designs a generative adversarial network to perform face anonymization and relationship cyphertexts embedding. In our framework, we first use Visual Anonymizer to manipulate the visual appearance of the input image, then use Cyphertext Embedder to get the anonymized image with the identity information embedded. With the help of Anonym Recognizer, the face recognition system can extract the relationship cyphertexts from the anonymized image as the credentials to match the identity information. The proposed Anonym-Recognizer provides a new perspective for the recognition and application of anonymized face images. Experiments on the Megaface dataset show that our method can encourage a 100% recognition accuracy on anonymized faces while finishing the task of face anonymization with high qualitative and quantitative quality.

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Cited By

View all
  • (2024)PerceptAnonProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693688(39955-39971)Online publication date: 21-Jul-2024
  • (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
  • (2023)Identifiable Face Privacy Protection via Virtual Identity TransformationIEEE Signal Processing Letters10.1109/LSP.2023.328939230(773-777)Online publication date: 2023

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    cover image ACM Conferences
    HCMA '22: Proceedings of the 3rd International Workshop on Human-Centric Multimedia Analysis
    October 2022
    106 pages
    ISBN:9781450394925
    DOI:10.1145/3552458
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 10 October 2022

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    Author Tags

    1. face anonymization
    2. face de-identification
    3. face generation
    4. face recognition
    5. privacy protection

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    HCMA '22 Paper Acceptance Rate 12 of 21 submissions, 57%;
    Overall Acceptance Rate 12 of 21 submissions, 57%

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    Cited By

    View all
    • (2024)PerceptAnonProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693688(39955-39971)Online publication date: 21-Jul-2024
    • (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
    • (2023)Identifiable Face Privacy Protection via Virtual Identity TransformationIEEE Signal Processing Letters10.1109/LSP.2023.328939230(773-777)Online publication date: 2023

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