copy-move forgery detection using fast retina keypoint (freak) descriptor

Image forgery, the manipulation of an image to hide some meaningful or helpful information, is widely used to manage the large amount of information being exchanged in the form of images. there are different forms of image forgery, and copy--move forgery is the most common form of it. the copy-move forgery is easy to perform yet challenging to detect due to the similarity between the original part of the image and the copied part. in this paper, we employ a keypoint descriptor inspired by the human visual system, namely the freak (fast retina keypoint) descriptor, for robust copy-move forgery detection. this method uses the advantages of freak descriptor such as fast computing and low memory load compared to sift, surf, and brisk. finally, geometric transformation parameters are extracted and discussed. results confirm promising results in the case of image post-processing operations such as adding noise, illumination change, and geometric transformations such as rotation and scaling.