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        Multimedia Forensics

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        Contributor(s)
        Sencar, Husrev Taha (editor)
        Verdoliva, Luisa (editor)
        Memon, Nasir (editor)
        Language
        English
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        Abstract
        This book is 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.
        URI
        https://library.oapen.org/handle/20.500.12657/54043
        Keywords
        Media Forensics; Digital Image Forensics; Video Forensics; Sensor Noise (PRNU); Deepfakes; Digital Integrity; Video Tampering Detection; Image Tampering Detection; ENF; Counter-Forensics
        DOI
        10.1007/978-981-16-7621-5
        ISBN
        9789811676215, 9789811676215
        Publisher
        Springer Nature
        Publisher website
        https://www.springernature.com/gp/products/books
        Publication date and place
        Singapore, 2022
        Imprint
        Springer Singapore
        Series
        Advances in Computer Vision and Pattern Recognition,
        Classification
        Computer security
        Image processing
        Computer vision
        Imaging systems and technology
        Machine learning
        Pages
        490
        Rights
        http://creativecommons.org/licenses/by/4.0/
        • Imported or submitted locally

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        License

        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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