Deepfake Detection using cnn
Akhil Sunil Kumar, Amruta khavase , Himesh Rajendran , Prof Suhas Lawand
convolutional Neural network (CNN), recurrent neural network (RNN)
In past months, free deep learning-based software tools have made the creation of credible face exchanges in videos that leave few traces of manipulation, in what are known as "DeepFake"(DF) videos. Manipulations of digital videos has been demonstrated for many years through the good use of visual effects, recent advances in deep learning have led to a drastic increase in the making real looking of fake content and the accessibility in which it can be created. These so-called AI-synthesized media .Creating the DeepFakes using Artificially intelligent tools are simple tasks. But, when it comes to detection of these DF, it is a major challenge. Because training the algorithm to spot the DeepFake is not simple. We have taken a step forward in detecting the DeepFakes using Convolutional Neural Network and Recurrent neural Network. System uses a convolutional Neural network to extract features at the frame level. These features are used to train a recurrent neural network which learns to classify if a video is manipulated or not and able to detect the temporary inconsistencies between frames introduced by the DeepFake creation tools. Expected result against a large set of fake videos collected from standard data sets. We show how our system can be competitive and results in using a simple architecture.
Article Details
Unique Paper ID: 151259

Publication Volume & Issue: Volume 7, Issue 12

Page(s): 412 - 415
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