Abstract & Overview
The rapid advancement of face image manipulation (FIM) algorithms and the proliferation of their user-friendly applications underscore an urgent need for manipulation detection techniques. These methods should be capable of revealing modifications in face images and substantiating their authenticity. Recently, the term "DeepFakes" and their detection techniques have attracted the attention of the research community. In addition, pay attention to the most recent techniques for detecting facial image manipulation that utilize watermarks. It's crucial to note that each of these techniques comes with its own set of limitations. This research aims to critically evaluate recent developments in face image authentication (FIA) methods, considering the widespread and user-friendly applications of FIM algorithms and their rapid growth. This study focuses on the urgent necessity for effective manipulation detection methods that can reliably identify and verify modifications in facial images, particularly with the rise of sophisticated DeepFakes. It explores two primary detection approaches: deep learning (DL) techniques, which leverage large datasets to detect subtle manipulations, and watermarking-based methods, which embed verification data into images to safeguard authenticity. The findings showcase the positive aspects and the limitations of these methods. DL techniques are powerful in detecting complex alterations but require substantial computational resources and data for training. Conversely, watermarking offers a proactive solution for verifying image integrity but may be vulnerable to advanced manipulation tactics and can impact image quality. The review emphasizes the importance of ongoing innovation, advocating for hybrid approaches that integrate the benefits of both DL and watermarking to overcome their shortcomings. This paper serves as a crucial reference for researchers, presenting a detailed overview of current trends, challenges, and future directions in face image manipulation detection (FIMD), underscoring the need for continuous development to keep pace with advancing manipulation technologies.