03978nam a22004935i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001100153050001400164072001700178072002300195072001600218100007500234245015200309250001800461264006900479300006700548336002600615337002600641338003600667347002400703505049400727520158101221650003302802650004202835650002902877650003502906650001302941650002902954700007002983700007103053700007403124700007203198710003403270773002603304776003603330776003603366776003603402856004603438978-3-031-58222-6DE-He21320251030112616.0cr nn 008mamaa240706s2024    sz |    s    |||| 0|eng d  a97830315822267 a10.1007/978-3-031-58222-62doi 4aQ337.5 4aTK7882.P3 7aUYQP2bicssc 7aCOM0160002bisacsh 7aUYQP2thema1 aWen, Yunqian.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aFace De-identification: Safeguarding Identities in the Digital Erah[electronic resource] /cby Yunqian Wen, Bo Liu, Li Song, Jingyi Cao, Rong Xie.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXVIII, 188 p. 53 illus., 49 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda0 aIntroduction -- 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.  aThis 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. . 0aPattern recognition systems. 0aData protectionxLaw and legislation. 0aArtificial intelligence.14aAutomated Pattern Recognition.24aPrivacy.24aArtificial Intelligence.1 aLiu, Bo.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut1 aSong, Li.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut1 aCao, Jingyi.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut1 aXie, Rong.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303158221908iPrinted edition:z978303158223308iPrinted edition:z978303158224040uhttps://doi.org/10.1007/978-3-031-58222-6