Deepfake Face Detection Using Deep InceptionNet Learning Algorithm
Prasannavenkatesan Theerthagiri, Ghouse basha Nagaladinne
Abstract
Deepfakes is digital manipulation techniques that use deep learning to produce deepfake (misleading) images and videos. Identifying deepfake images is the most difficult part of finding the original. Due to the increasing reputation of deep fakes, identifying original images and videos is more crucial to detect manipulated videos. This paper studies and experiments with different methods that can be used to detect fake and real images and videos. The Convolutional Neural Network (CNN) algorithm named InceptionNet has been used to identify deep fakes. A comparative analysis was performed in this work based on various convolutional Networks. This work uses the dataset from Kaggle with 401 videos of train sample and 3745 images were generated by augmentation process. The results were evaluated with the metrics like accuracy and confusion matrix. The results of the proposed model produces better results in terms of accuracy with 93 % on identifying deep fake images and videos