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    Detecting Deepfake Videos using Digital Watermarking

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    Publication date
    2021-12
    End of Embargo
    2022-12-15
    Author
    Qureshi, Amna
    Megías, D.
    Kuribayashi, M.
    Keyword
    Signal processing algorithms
    Watermarking
    Information processing
    Signal processing
    Software
    Robustness
    Fake news
    Rights
    © 2021 IEEE. Reproduced in accordance with the publisher's self-archiving policy.
    Peer-Reviewed
    Yes
    Open Access status
    embargoedAccess
    
    Metadata
    Show full item record
    Abstract
    Deepfakes constitute fake content -generally in the form of video clips and other media formats such as images or audio- created using deep learning algorithms. With the rapid development of artificial intelligence (AI) technologies, the deepfake content is becoming more sophisticated, with the developed detection techniques proving to be less effective. So far, most of the detection techniques in the literature are based on AI algorithms and can be considered as passive. This paper presents a proof-of-concept deepfake detection system that detects fake news video clips generated using voice impersonation. In the proposed scheme, digital watermarks are embedded in the audio track of a video using a hybrid speech watermarking technique. This is an active approach for deepfake detection. A standalone software application can perform the detection of robust and fragile watermarks. Simulations are performed to evaluate the embedded watermark's robustness against common signal processing and video integrity attacks. As far as we know, this is one of the first few attempts to use digital watermarking for fake content detection.
    URI
    http://hdl.handle.net/10454/18796
    Version
    Accepted manuscript
    Citation
    Qureshi A, Megías D and Kuribayashi M (2021) Detecting Deepfake Videos using Digital Watermarking. Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) 14-17 Dec 2021. Tokyo, Japan: 1786-1793.
    Link to publisher’s version
    https://ieeexplore.ieee.org/document/9689555
    Type
    Conference paper
    Notes
    The full-text of this article will be released for public view at the end of the publisher embargo on 15 Dec 2022.
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    Engineering and Informatics Publications

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