Copy-Move Image Forgery Detection Using Deep Learning Approaches: An Abbreviated Survey

Abstract

Images play a fundamental role in digital media, and altering digital images can present a significant risk since it contributes to disseminating false information. The rapid advancement of technology in digital image forensics has significantly improved the quality of forged images to the extent that many forgeries are now indistinguishable. Digital image authenticity and reliability are becoming more significant as evidence. Some people invalidate photos by adding or removing sections. Therefore, image forgery detection and localization are crucial. Image manipulation techniques have made this a major computer vision issue. Images can be obtained from many origins and may appear in multiple formats. Consequently, passive techniques for detecting image forgeries are generally favored, which do not necessitate prior knowledge about an image. The prevalent forms of passive image forgery detection encompass the identification of copy-move and image-splicing forgeries. Recently, deep learning techniques have become prevalent in image manipulation detection. These techniques demonstrated superior accuracy to traditional approaches due to their ability to extract features from images effectively. This study comprehensively examines deep learning methodologies utilized in detecting copy-move forgery, and it has mostly focused on studies conducted in recent years, and data sets commonly used in detecting copy-move forgery have been mentioned. This abbreviated survey concluded that combining conventional image processing methods with pre-trained CNN approaches could leverage the strength of both, exhibit significant efficiency, and decrease the requirement for labeled image datasets. Furthermore, utilizing ensemble techniques to integrate multiple approaches improves overall forgery detection performance.