Copy-move forgery detection using on locality sensitive hashing and k-means clustering

Digital images are a main source of information in our modern digital era. However, the easiness of manipulating digital images using simple userfriendly software makes the credibility of images questionable. Copy-Move is one of the most common image forgery types, where a region of an image is copied and pasted into another location of the same image. Such a forgery is simple to achieve but hard to be detected as the pasted region shares the same characteristics with the image. Although plenty of algorithms have been proposed to tackle the copy-move detection problem, a fast and reliable copy-move detection algorithm is not achieved yet. In this paper, a new matching method is proposed which can reduce the detection time and enhance the accuracy of detection as well. Such enhancement is done by clustering image blocks into clusters, and searching for identical blocks within each cluster instead of all image blocks. For that purpose, k-means clustering is used to cluster the image blocks then Locality Sensitive Hashing (LSH) method is used to match the blocks based on Zernike moments. The experimental results shows that the processing time has been reduced to 10% and the detection accuracy has been enhanced as well.