Divide and Conquer: a Two-Step Method for High Quality Face De-identification with Model Explainability
Author:
Affiliation:
1. Shanghai Jiao Tong University,Institute of Image Communication and Network Engineering
2. University of Technology Sydney,School of Computer Science
Funder
Fundamental Research Funds for the Central Universities
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10376473/10376477/10377576.pdf?arnumber=10377576
Reference62 articles.
1. Person De-Identification in Videos
2. DeepPrivacy: A Generative Adversarial Network for Face Anonymization
3. CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks
4. AnonymousNet: Natural Face De-Identification With Measurable Privacy
5. Password-Conditioned Anonymization and Deanonymization with Face Identity Transformers
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1. Face De-Identification Using Convolutional Neural Network (CNN) Models for Visual-Copy Detection;Applied Sciences;2024-02-21
2. Future Prospects and Challenges;Face De-identification: Safeguarding Identities in the Digital Era;2024
3. Introduction;Face De-identification: Safeguarding Identities in the Digital Era;2024
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