Wen, Yunqian. creator author. aut http://id.loc.gov/vocabulary/relators/aut Liu, Bo. author. aut http://id.loc.gov/vocabulary/relators/aut Song, Li. author. aut http://id.loc.gov/vocabulary/relators/aut Cao, Jingyi. author. aut http://id.loc.gov/vocabulary/relators/aut Xie, Rong. author. aut http://id.loc.gov/vocabulary/relators/aut SpringerLink (Online service) text sz 2024 1st ed. 2024. monographic eng

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access XVIII, 188 p. 53 illus., 49 illus. in color. online resource. 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. . 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. by Yunqian Wen, Bo Liu, Li Song, Jingyi Cao, Rong Xie. Pattern recognition systems Data protection Law and legislation Artificial intelligence Automated Pattern Recognition Privacy Artificial Intelligence Q337.5 TK7882.P3 9783031582226 https://doi.org/10.1007/978-3-031-58222-6 https://doi.org/10.1007/978-3-031-58222-6 240706 20251030112616.0 978-3-031-58222-6