A comparative study of deepfake facial manipulation technique using generative adversarial networks

The manipulation of digital images has become a popular trend. Due to the development of image processing tools and visualization techniques integrating deep learning and artificial intelligence (AI) algorithms (in particular generative adversarial networks–GAN), this can pose serious threats to privacy and security. In recent years, Deepfake algorithms have been designed to exchange faces or modify facial features, potentially leading to more severe problems in this context. In this manuscript, we provide a comprehensive review of the two most important facial image processing technologies: (i) deepfake face manipulation; and (ii) face manipulation detection techniques. Furthermore, we explore the state-of-the-art of popular GAN techniques. In particular, three types of Deepfake face detection technologies are reviewed: (i) hand-crafted features (ii) artifacts; and (iii) learning-based features, while highlighting related improvements and challenges. Furthermore, this article discusses potential challenges and promising research directions for future investigation. We believe that this review has been organized to provide a structured analysis of important research papers and to discuss each study’s main findings and conclusions. Our investigation reveals shortcomings in manipulation detection benchmarks due to real-world scenario variations and biased dataset comparisons. Current research priorities revolve around enhancing GAN training stability, resolution, and manipulable facial features. Moreover, GANs have shown superior results in identifying fake images; however, their reliance prompts a systematic approach to detecting fakes. This dependency raises questions about detecting fake images with or without manipulated GAN architecture, urging the need for novel computational techniques to identify manipulations without GAN assistance.