## 1 Introduction  
Over the past few years, the field of digital forensics has arisen to help restore some trust to digital images that appear all over the internet in an era of what is colloquially called \"fake news\". Every day, vast amounts of multimedia content is generated through a variety of devices and shared via websites, magazines, newspapers, and television. Among them, image plays a crucial role regarding communication because it is considered one of the most influential communication media, which dominates most social network sites, mostly because existing mobile devices allow people to capture high-quality images anywhere at any time. Unfortunately, we cannot fully trust digital images as before. Given the advancement in computer vision in recent years, it has been easier to edit an image by using image processing and computer graphic techniques. Moreover, with the extensive availability of advanced image editing tools such as Adobe Photoshop and Gimp, altering a digital photo, with few signs or no visible sign of editing, has become more accessible and common. Generative adversarial networks (GANs), a class of artificial intelligence algorithms used in unsupervised learning, are emerging as a hot topic in the research community because of their ability to generate photographs that look at least superficially authentic to human observers, having many realistic characteristics. Some digital images generated by a GAN model are shown in Figure 1. Even after a close inspection, people may mistakenly identify a computer-generated image as original. The consequence would become even more severe if the images are used for commercial or political motives. As a result, there is an urgent need to develop a system which can verify an image's authenticity. The verification process can be as simple as checking whether an image is generated by a computer (Classification), or as complicated as pointing out precisely which parts of the image are edited (Segmentation). The images generated by GANs are becoming highly realistic [1; 2]. However, each GAN model serves a specific topic in machine vision, and the generated images were varied in size and resolution. For any GAN, the optimal classifier belongs to its discriminator. Moreover, research focused on the detection of GAN-generated images has been limited. Thus, we propose a robust deep learning model which can detect images generated by multiple GAN models. One of the biggest challenges GANs and other research topics have to deal with is the imbalance scenario, as the number of samples belonging to one class is much lower than those belonging to the other classes. There are different approaches to solving the imbalanced issue, such as resampling (i.e., trying to get an equal number of samples for both classes by increasing the frequency of the minority class and reducing the frequency of the majority class. ), or ensemble (i.e., enhancing the classifier's performance by building several two-stage classifiers from the original data and then adding up their predictions). This paper proposes a system that can detect the face image generated by GAN networks. The main contributions of this work are as follows: (1) We propose a customized deep learning model (CGFace) that is specially designed for computer-generated detection task; (2) The boosting algorithms are applied  
Figure 1: Example of computer-generated images generated by GAN (One of these is a photo of a real face, other faces are completely created by a GAN). on CGFace to detect computer-generated images efficiently (3) We implement two state-of the-art methods for generating computer-generated face images to create two huge datasets; and (4) Various experiments were conducted to examine the proposed model in (1) on the collected dataset in (2) to test the proposed model's ability to detect images generated by different GAN models. The rest of the paper is divided as follows. In Section 2, we thoroughly survey previous approaches to generative adversarial networks and computer-generated image problems.