# Tampered and Computer-Generated Face Images Identification Based on Deep Learning
## 1 Introduction  
A social networking service (SNS) provides various online services for its users to connect with friends, families, classmates, and other people who share similar hobbies, careers, and backgrounds. According to today's trend, social networking is one of the easiest ways for a person to communicate with other people online, and it has transformed the way people create, maintain, and sustain their social information network [1]. An immense amount of data is uploaded on social networking sites, such as Facebook, YouTube, Instagram, and Twitter every day [2]. Compared to text materials and videos, photographs are a more straightforward means to convey information. Originally, most of the images uploaded on social network platforms are genuine because users capture life moments and share these moments on social networks. However, the threats of fake news have become more more and more serious in recent years [3]. Image tampering is an effective technique that can be exploited to manipulate images. There are three standard techniques in image tampering, including copy-move, image splicing, and image retouching [4]. The copy-move method refers to the process of copying some parts from a source image and putting them into a target image, whereas the image splicing technique combines two or more images to create a composite image. On the other hand, the image retouching technique applies several computer vision (CV) technologies to create a new image by enhancing some features from the original image [5]. After conducting these techniques, enhancement of boundary, shape, scaling, and illumination for the created image is implemented to minimize the defects and make it more challenging to identify the tampered regions. To make it even more difficult, generative adversarial networks (GANs), a new branch of unsupervised learning artificial intelligence (AI) has emerged as a hot topic in recent years. It can generate photographs that have realistic characteristics and look superficially authentic to human observers [5; 6]. Output images of these techniques spread like wildfire on the internet due to the development of social networks [6]. Figure 1 represents four digital image tampering examples, which concentrate especially on manipulating the facial parts. Even after a close inspection, there is a high possibility that observers are unable to detect the tampered regions. If these images are fed into conventional face detection and recognition frameworks, the faces from manipulated images are detected and recognized as authentic images [7]. The consequences become even more severe if manipulated images are used for commercial or political intentions. Even though researchers dedicated to conducting image tampering detection have increased sharply in recent years, many drawbacks still exist in previous frameworks. Existing models were designed to recognize specific characteristics of the dataset under consideration. For example, in error level analysis (ELA), the actual interpretation of the level of compression artifacts in a given segment of an image is biased, which can lead to inaccurate judgment [8]. The color filter array (CFA) method is vulnerable to images that were resampled onto a CFA and then re-interpolated. Moreover, pixels that are close to the digital sensor resolution limit can be a problem for CFA [9]. Double JPEG localization technique is vulnerable to tampered images that went through many post-processing  
Figure 1: Example of digital image forgery methods (**a**) copy-move forgery method, (**b**) image retouching method, (**c**) image splicing method, and (**d**) generative adversarial networks (GANs) method. steps [10]. For GANs, each model generates new data instances on a particular topic, and the generated instances have different sizes and resolutions. In addition, GAN-generated images identification research is limited.