Manual and Automatic Feature Engineering in Digital Image Forgery Detection Algorithms: Survey

Abstract

Digital image forgery (DIF) detection has gained importance in recent years. Because of the availability of lowcost, high-resolution digital cameras, issues related to confirming the authenticity of digital images have been highlighted. The term ‘‘ manipulation’’ refers to the changing of any image’s objects or contents without giving any indication that anything has changed. In general, image splicing is one of the most popular techniques used to fake images. Image splicing is the process of adding or hiding things by copying portions of one image and pasting them elsewhere in the same image or into a different image. There have been several image splicing forgery detection surveys throughout the past few decades. However, neither the image forgery detection algorithms-based manual engineering features nor the image forgery detection algorithms-based automatic engineering features are covered by these surveys. This research provided a comparative analysis of image splicing by gathering several techniques and classifying them according to the features they employed, based on manual engineering features and automatic engineering features.