| Summary: | Generative adversarial networks (GANs) describe an emerging generative model which has made impressive progress in the last few years in generating photorealistic facial images. As the result, it has become more and more difficult to differentiate between computer-generated and real face images, even with the human’s eyes. If the generated images are used with the intent to mislead and deceive readers, it would probably cause severe ethical, moral, and legal issues. Moreover, it is challenging to collect a dataset for computer-generated face identification that is large enough for research purposes because the number of realistic computer-generated images is still limited and scattered on the internet. Thus, a development of a novel decision support system for analyzing and detecting computer-generated face images generated by the GAN network is crucial. In this paper, we propose a customized convolutional neural network, namely CGFace, which is specifically designed for the computer-generated face detection task by customizing the number of convolutional layers, so it performs well in detecting computer-generated face images. After that, an imbalanced framework (IF-CGFace) is created by altering CGFace’s layer structure to adjust to the imbalanced data issue by extracting features from CGFace layers and use them to train AdaBoost and eXtreme Gradient Boosting (XGB). Next, we explain the process of generating a large computer-generated dataset based on the state-of-the-art PCGAN and BEGAN model. Then, various experiments are carried out to show that the proposed model with augmented input yields the highest accuracy at 98%. Finally, we provided comparative results by applying the proposed CNN architecture on images generated by other GAN researches.
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