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Face de-identification = safeguardin...

Wen, Yunqian.

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Face de-identification = safeguarding identities in the digital era /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Face de-identification/ by Yunqian Wen ... [et al.].
Reminder of title:	safeguarding identities in the digital era /
other author:	Wen, Yunqian.
Published:	Cham :Springer Nature Switzerland : : 2024.,
Description:	xviii, 188 p. :ill. (chiefly col.), digital ;24 cm.
[NT 15003449]:	Introduction -- Facial Recognition Technology and the Privacy Risks -- Overview of Face De-identification Techniques -- Face Image Privacy Protection with Differential Private k-anonymity -- Differential Private Identification Protection for Face Images -- Personalized and Invertible Face De-identification -- High Quality Face De-identification with Model Explainability -- Deep Motion Flow Guided Reversible Face Video De-Identification -- Future Prospects and Challenges -- Conclusion.
Contained By:	Springer Nature eBook
Subject:	Privacy-preserving techniques (Computer science) -
Online resource:	https://doi.org/10.1007/978-3-031-58222-6
ISBN:	9783031582226

Face de-identification = safeguarding identities in the digital era /
Face de-identificationsafeguarding identities in the digital era /[electronic resource] :by Yunqian Wen ... [et al.]. - Cham :Springer Nature Switzerland :2024. - xviii, 188 p. :ill. (chiefly col.), digital ;24 cm.

Introduction -- Facial Recognition Technology and the Privacy Risks -- Overview of Face De-identification Techniques -- Face Image Privacy Protection with Differential Private k-anonymity -- Differential Private Identification Protection for Face Images -- Personalized and Invertible Face De-identification -- High Quality Face De-identification with Model Explainability -- Deep Motion Flow Guided Reversible Face Video De-Identification -- Future Prospects and Challenges -- Conclusion.

This book provides state-of-the-art Face De-Identification techniques and privacy protection methods, while highlighting the challenges faced in safeguarding personal information. It presents three innovative image privacy protection approaches, including differential private k-anonymity, identity differential privacy guarantee and personalized and invertible Face De-Identification. In addition, the authors propose a novel architecture for reversible Face Video De-Identification, which utilizes deep motion flow to ensure seamless privacy protection across video frames. This book is a compelling exploration of the rapidly evolving field of Face De-Identification and privacy protection in the age of advanced AI-based face recognition technology and pervasive surveillance. This insightful book embarks readers on a journey through the intricate landscape of facial recognition, artificial intelligence, social network and the challenges posed by the digital footprint left behind by individuals in their daily lives. The authors also explore emerging trends in privacy protection and discuss future research directions. Researchers working in computer science, artificial intelligence, machine learning, data privacy and cybersecurity as well as advanced-level students majoring in computers science will find this book useful as reference or secondary text. Professionals working in the fields of biometrics, data security, software development and facial recognition technology as well as policymakers and government officials will also want to purchase this book.

ISBN: 9783031582226

Standard No.: 10.1007/978-3-031-58222-6doiSubjects--Topical Terms:

3722245
Privacy-preserving techniques (Computer science)

LC Class. No.: QA76.9.P735

Dewey Class. No.: 005.8

Face de-identification = safeguarding identities in the digital era /
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245 0 0 $a Face de-identification $h [electronic resource] : $b safeguarding identities in the digital era / $c by Yunqian Wen ... [et al.].
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300 $a xviii, 188 p. : $b ill. (chiefly col.), digital ; $c 24 cm.
505 0 $a Introduction -- Facial Recognition Technology and the Privacy Risks -- Overview of Face De-identification Techniques -- Face Image Privacy Protection with Differential Private k-anonymity -- Differential Private Identification Protection for Face Images -- Personalized and Invertible Face De-identification -- High Quality Face De-identification with Model Explainability -- Deep Motion Flow Guided Reversible Face Video De-Identification -- Future Prospects and Challenges -- Conclusion.
520 $a This book provides state-of-the-art Face De-Identification techniques and privacy protection methods, while highlighting the challenges faced in safeguarding personal information. It presents three innovative image privacy protection approaches, including differential private k-anonymity, identity differential privacy guarantee and personalized and invertible Face De-Identification. In addition, the authors propose a novel architecture for reversible Face Video De-Identification, which utilizes deep motion flow to ensure seamless privacy protection across video frames. This book is a compelling exploration of the rapidly evolving field of Face De-Identification and privacy protection in the age of advanced AI-based face recognition technology and pervasive surveillance. This insightful book embarks readers on a journey through the intricate landscape of facial recognition, artificial intelligence, social network and the challenges posed by the digital footprint left behind by individuals in their daily lives. The authors also explore emerging trends in privacy protection and discuss future research directions. Researchers working in computer science, artificial intelligence, machine learning, data privacy and cybersecurity as well as advanced-level students majoring in computers science will find this book useful as reference or secondary text. Professionals working in the fields of biometrics, data security, software development and facial recognition technology as well as policymakers and government officials will also want to purchase this book.
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650 1 4 $a Automated Pattern Recognition. $3 3538549
650 2 4 $a Privacy. $3 528582
650 2 4 $a Artificial Intelligence. $3 769149
700 1 $a Wen, Yunqian. $3 3722244
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