rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Multimedia Forensics
  name:
    name part: Sencar, Husrev Taha.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Verdoliva, Luisa.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Memon, Nasir.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: si
    date issued: 2022
    edition: 1st ed. 2022.
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (XII, 490 pages 151 illustrations, 137 illustrations in color.)
  abstract: This book is an open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field.
  table of contents: What's In This Book and Why? -- Media Forensics in the Age of Disinformation -- Computational Imaging -- Sensor Fingerprints: Camera Identification and Beyond -- Source Camera Attribution from Videos -- Source Camera Model Identification -- GAN Fingerprints in Face Image Synthesis -- Physical Integrity.
  note: edited by Husrev Taha Sencar, Luisa Verdoliva, Nasir Memon.
  note: Open Access
  subject:
    topic: Data protection
  subject:
    topic: Computer vision
  subject:
    topic: Image processing
  subject:
    topic: Machine learning
  subject:
    topic: Multimedia systems
  subject:
    topic: Data and Information Security
  subject:
    topic: Computer Vision
  subject:
    topic: Image Processing
  subject:
    topic: Machine Learning
  subject:
    topic: Security Services
  subject:
    topic: Multimedia Information Systems
  classification: QA76.9.A25
  classification: 005.8
  related item:
    title info:
      title: Springer Nature eBook
  related item:
    title info:
      title: Advances in Computer Vision and Pattern Recognition
  identifier: 9789811676215
  identifier: https://doi.org/10.1007/978-981-16-7621-5
  access condition: Open Access
  record info:
    record creation date: 220401
    record change date: 20230829020254.3
    record identifier: in00004577730