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@article{156985,
        author = {Siva Prasad Patnayakuni },
        title = {Detection of A Facial Forgery Video with MesoNet},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {5},
        pages = {570-577},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=156985},
        abstract = {This paper gives a technique to routinely and correctly locate face tampering in films, and specifically makes a speciality of current strategies used to generate hyper sensible cast films: Deepfake and Face2Face. Traditional photo forensics strategies are normally now no longer nicely ideal to films because of the compression that strongly degrades the data. Thus, this paper follows a deep getting to know technique and gives networks, each with a low quantity of layers to attention at the mesoscopic residences of images. We examine the ones rapid networks on each present dataset and a dataset we've got constituted from on-line films. The assessments reveal a completely a hit detection fee with extra than 98.4% for Deepfake and 95.3% for Face2Face.},
        keywords = {Facial forgery, MesoNet, Deepfake, Face2face, Neural Network .},
        month = {},
        }