04351nam a22005775i 4500 978-3-031-58222-6 DE-He213 20251030112616.0 cr nn 008mamaa 240706s2024 sz | s |||| 0|eng d 9783031582226 978-3-031-58222-6 10.1007/978-3-031-58222-6 doi Q337.5 TK7882.P3 UYQP bicssc COM016000 bisacsh UYQP thema Wen, Yunqian. author. aut http://id.loc.gov/vocabulary/relators/aut 80139 Face De-identification: Safeguarding Identities in the Digital Era [electronic resource] / by Yunqian Wen, Bo Liu, Li Song, Jingyi Cao, Rong Xie. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XVIII, 188 p. 53 illus., 49 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda 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. . Pattern recognition systems. 65547 Data protection Law and legislation. 66490 Artificial intelligence. 58441 Automated Pattern Recognition. 65551 Privacy. 66493 Artificial Intelligence. 58441 Liu, Bo. author. aut http://id.loc.gov/vocabulary/relators/aut 67514 Song, Li. author. aut http://id.loc.gov/vocabulary/relators/aut 80140 Cao, Jingyi. author. aut http://id.loc.gov/vocabulary/relators/aut 80141 Xie, Rong. author. aut http://id.loc.gov/vocabulary/relators/aut 80142 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031582219 Printed edition: 9783031582233 Printed edition: 9783031582240 https://doi.org/10.1007/978-3-031-58222-6 ZDB-2-SCS ZDB-2-SXCS 61396 61396