ISSN 2394-5125

    Image Forgery Detection Using Deep Learning (2023)

    T. Sarika, Annreddy Deekshitha, Devesh Patel , Akhilesh Chappalabanda, Bejavada Kumar Datta
    JCR. 2023: 349-358


    In this digital era, images and videos are being used as influential sources of evidence in a variety of contexts like evidence during trials, insurance fraud, social networking, etc. The easy adaptability of editing tools for digital images, especially without any visual proof of manipulation, give rise to questions about their authenticity. It is the job of image forensics authorities to develop technological innovations that would detect the forgeries of images. There are three primary classes of manipulation or forgery detectors studies until now: those supported features descriptors, those supported inconsistent shadows and eventually those supported double JPEG compression.


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    Volume & Issue

    Volume 10 Issue-3