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@article{159374,
        author = {Kamla Vishwakarma  and Prince Kori and Sushant Dhawane  and Yogita Chavan },
        title = {Deepfake detection using deeplearning },
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {11},
        pages = {981-984},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=159374},
        abstract = {Increasing computing power has made deep learning algorithms so powerful that creating a fake video generated by artificial intelligence, popularly called as deep fakes, is very simple. Scenarios where this realistic face has been replaced by deep fakes are used to create political unrest, fake terrorist acts, revenge porn, blackmailing nations can easily be imagined. In this work,  a new method is based on deep learning that can effectively distinguish fake videos generated by artificial intelligence from real videos. This method is able to automatically detect replacement and reenactment of deep forgery. Using artificial intelligence (AI) to fight against artificial intelligence (AI). This system uses Res-Next Convolution Neural Network to extract frame-level features and these features and further uses Long-Short-Term Memory (LSTM)-based Recurrent Neural Network (RNN) to classify whether the video is subject to some kind of manipulation or not, i.e. .whether the video is deepfake or real video. System can also achieve competitive results using a very simple and robust approach.},
        keywords = {Res-next convolution neural network , RNN, LSTM (Long short-term memory)},
        month = {},
        }