AI AND DEEPFAKE SYNTHETIC MEDIA
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Date
2021
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Publisher
UMT, Lahore
Abstract
From the last two to three year has marked as a fast growth of DeepFake synthetic videos. The biggest challenge for the research community to the detections of DeepFake videos. The aim of this research is to classify the videos whether they are real or fake that can be used to robustly identify the face image in videos. A deep convolutional neural network (CNN) reframed model Multitask-Cascading Neural network (MTCNN) and trained on face area of image getting from videos frames. each videos have 300 frames of face images. A pre-trained model structure similarity is used for classification. On Training model results shows the accuracy of 80% by using 400 videos. A dataset must be larger needed to overcome the overfitting of model and increase the accuracy of model. When sufficient classification accuracies are reached, smart picking methods can be implemented to efficiently handle DeepFake videos.