Customized Convolutional Neural Network for Accurate Detection of Deep Fake Images in Video Collections

The motivation for this study is that the quality of deep fakes is constantly improving, which leads to the need to develop new methods for their detection. The proposed Customized Convolutional Neural Network method involves extracting structured data from video frames using facial landmark detection, which is then used as input to the CNN. The customized Convolutional Neural Network method is the date augmented-based CNN model to generate ‘fake data’ or ‘fake images’. This study was carried out using Python and its libraries. We used 242 films from the dataset gathered by the Deep Fake Detection Challenge, of which 199 were made up and the remaining 53 were real. Ten seconds were allotted for each video. There were 318 videos used in all, 199 of which were fake and 119 of which were real. Our proposed method achieved a testing accuracy of 91.47%, loss of 0.342, and AUC score of 0.92, outperforming two alternative approaches, CNN and MLP-CNN. Furthermore, our method succeeded in greater accuracy than contemporary models such as XceptionNet, Meso-4, EfficientNet-BO, MesoInception-4, VGG-16, and DST-Net. The novelty of this investigation is the development of a new Convolutional Neural Network (CNN) learning model that can accurately detect deep fake face photos.