Digital Face Manipulation Creation and Detection: A Systematic Review

Abstract

The introduction of publicly available large-scale datasets and advances in generative adversarial networks (GANs) have revolutionized the generation of hyper-realistic facial images, which are difficult to detect and can rapidly reach millions of people, with adverse impacts on the community. Research on manipulated facial image detection and generation remains scattered and in development. This survey aimed to address this gap by providing a comprehensive analysis of the methods used to produce manipulated face images, with a focus on deepfake technology and emerging techniques for detecting fake images. The review examined four key groups of manipulated face generation techniques: (1) attributes manipulation, (2) facial re-enactment, (3) face swapping, and (4) face synthesis. Through an in-depth investigation, this study sheds light on commonly used datasets, standard manipulated face generation/detection approaches, and benchmarking methods for each manipulation group. Particular emphasis is placed on the advancements and detection techniques related to deepfake technology. Furthermore, the paper explores the benefits of analyzing deepfake while also highlighting the potential threats posed by this technology. Existing challenges in the field are discussed, and several directions for future research are proposed to tackle these challenges effectively. By offering insights into the state of the art for manipulated face image detection and generation, this survey contributes to the advancement of understanding and combating the misuse of deepfake technology.