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020 _a9783031582226
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024 7 _a10.1007/978-3-031-58222-6
_2doi
050 4 _aQ337.5
050 4 _aTK7882.P3
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072 7 _aCOM016000
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072 7 _aUYQP
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100 1 _aWen, Yunqian.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980139
245 1 0 _aFace De-identification: Safeguarding Identities in the Digital Era
_h[electronic resource] /
_cby Yunqian Wen, Bo Liu, Li Song, Jingyi Cao, Rong Xie.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXVIII, 188 p. 53 illus., 49 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
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505 0 _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.
520 _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. .
650 0 _aPattern recognition systems.
_965547
650 0 _aData protection
_xLaw and legislation.
_966490
650 0 _aArtificial intelligence.
_958441
650 1 4 _aAutomated Pattern Recognition.
_965551
650 2 4 _aPrivacy.
_966493
650 2 4 _aArtificial Intelligence.
_958441
700 1 _aLiu, Bo.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_967514
700 1 _aSong, Li.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980140
700 1 _aCao, Jingyi.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980141
700 1 _aXie, Rong.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980142
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031582219
776 0 8 _iPrinted edition:
_z9783031582233
776 0 8 _iPrinted edition:
_z9783031582240
856 4 0 _uhttps://doi.org/10.1007/978-3-031-58222-6
912 _aZDB-2-SCS
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999 _c61396
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