detection of copy-move forgery in digital images using scale invariant feature transform algorithm and the spearman relationship

Increased popularity of digital media and image editing software has led to the spread of multimedia content forgery for various purposes. undoubtedly, law and forensic medicine experts require trustworthy and nonforged images to enforce rights. copymove forgery is the most common type of manipulation of digital images. copymove forgery is used to hide an area of the image or to repeat a portion in the same image. in this paper, a method is presented for detecting copymove forgery using the scaleinvariant feature transform (sift) algorithm. the spearman relationship and ward clustering algorithm are used to measure the similarity between keypoints, also to increase the accuracy of forgery detection. this method is invariant to changes such as rotation, scale change, deformation, and light change; it falls into the category of blind forgery detection methods. the experimental results show that with its high resistance to apparent changes, the proposed method correctly detects 99.56 percent of the forged images in the dataset and reveals the forged areas.