Convolutional Neural Networks for Real and Fake Face Classification

This paper deals with the problem of classifying images of real and fake faces as it is impossible to distinguish them with the bare eye. Two different convolutional neural networks architecture models are applied. The first one is pre-trained VGG16 model, where transfer learning method is applied on our dataset. The architecture of the second model is based on VGG16 and represents its smaller and lighter version. Techniques such as learning rate decay, dropout and batch normalization was applied in training process. Comparison of obtained results of both models is made.