Deepfake Video Detection and Fake News Detection using Machine Learning

Mudit Singh, Abhishek Kumar Bharti, Sailesh Singh, Rishabh Shukla; Mr. Ketan Anand

Deepfake Video Detection and Fake News Detection using Machine Learning

Authors: Mudit Singh, Abhishek Kumar Bharti, Sailesh Singh, Rishabh Shukla, Mr. Ketan Anand

Unique Paper ID: 164937
Volume: 10
Issue: 12
PageNo: 2951-2955

Keywords: DF Video Detection convolutional Neural network (CNN) recurrent neural network (RNN)

Abstract:
In recent months, free deep literacy- grounded software tools have eased the creation of believable face exchanges in videos that leave many traces of manipulation, in what they're known as" DF"(DF) videos. Manipulations of digital videos have been demonstrated for several decades through the good use of visual goods, recent advances in deep literacy have led to a drastic increase in the literalism of fake content and the availability in which it can be created. These so- called AI- synthesized media(popularly appertained to as DF). Creating the DF using the Instinctively intelligent tools are simple task. But, when it comes to discovery of these DF, it's major challenge. Because training the algorithm to spot the DF isn't simple. We've taken a step forward in detecting the DF using Convolutional Neural Network and intermittent neural Network. System uses a convolutional Neural network(CNN) to prize features at the frame position. These features are used to train a intermittent neural network(RNN) which learns to classify if a videotape has been subject to manipulation or not and suitable to descry the temporal inconsistencies between frames introduced by the DF creation tools. Anticipated result against a large set of fake videos collected from standard data set. We show how our system can be competitive result in this task results in using a simple armature.

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ISSN: 2349-6002
Volume: 10
Issue: 12
PageNo: 2951-2955

Deepfake Video Detection and Fake News Detection using Machine Learning

Available:https://ijirt.org/Article?manuscript=164937

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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