Description

The easy accessibility of advanced image editing software and powerful computing hardware. Various types of forgeries can be created and in recent years, image forgery detection using passive techniques. most common types of image forgeries is the copy–paste (or copy–move or cloning) forgery, where a region from one part of an image is copied and pasted onto another part, thereby concealing the image content in the latter region. Image manipulation has become commonplace with growing easy access to powerful computing abilities. One of the most common types of image forgeries is the copy–paste forgery, wherein a region from an image is replaced with another region from the same image. Most prior approaches to finding identical regions suffer from their inability to detect the cloned region when it has been subjected to a geometric transformation. In this paper, we propose a novel technique based on transform-invariant features. A feature matching accuracy in excess of 90% across postprocessing operations and are able to detect the cloned regions with a high true positive rate and lower false positive rate than the state of the art.