Deepfakes Classification of Faces Using Convolutional Neural Networks
Jatin Sharma, Sahil Sharma, Vijay Kumar, Hany S. Hussein, Hammam Alshazly
Abstract
In the recent years, petabytes of data is being generated and uploaded online every second. To successfully detect fake contents, a deepfake detection technique is used to determine whether the uploaded content is real or fake. In this paper, a convolutional neural network-based model is proposed to detect the fake face images. The generative adversarial networks and data augmentation are used to generate the face dataset for real and fake face classification. Transfer learning techniques from pretrained deep models such as VGG16 and ResNet50 are employed in the proposed model. The proposed model is evaluated on three benchmark datasets, namely 140k Real and Fake Faces, Real and Fake Face Detection, and Fake Faces. The proposed model attained accuracies over the three datasets are 95.85%, 53.25%, and 88.63%, respectively. Moreover, to improve the obtained results of the proposed model, we combine it with other pretrained models of VGG16 and ResNet50 to construct deep ensembles. The overall performance is greatly improved with the ensemble model achieving accuracies on the three datasets as 98.79%, 75.79%, and 95.52%, respectively. Furthermore, the obtained results also show that the proposed models have superior performance than existing models.