A Survey on Deepfake Detection Techniques
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Abstract
With the recent advancement in the Deep Learning algorithm, its application has been broadened over various fields ranging from big data analytics to human biometric systems. One such field where it takes up the hand is the implementation of various application like FaceApp, FakeApp etc. that is used in the generation of manipulated media files which is termed as Deepfake. These application has a wider increase in its popularity among common public due to its user friendly features and are used in various domains like Digital Fraud, Cyber Crimes, Politics and in even in Military Activities. So, it is very much important to develop some kind of detection techniques that can take away this kind of forgeries and put up a new step in video and audio forensics. In this paper, we present the various creation and detection techniques that is up now in research in Deepfake using various techniques like Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short Term Memory (LSTM) etc. and thus provide a backbone in the implementation of a new scheme that would be more compactable and accurate in the detection of Deepfakes. Also we done a comparison study on various techniques under traditional and state of the art approach and brought up a conclusion that most of the techniques under traditional approach are time consuming process, need expertise knowledge over the technology for the user to use them etc. where as in the case of state of the art approach, the techniques require less time for processing and anyone with less knowledge over the technology can use this technologies for the creation and detection of Deepfake
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