A proposed accelerated image copy-move forgery detection

Image forgery detection is currently one of the interested research fields of image processing. Copy-Move (CM) forgery is one of the frequently used techniques. In this paper, we propose a method which is efficient and fast for detect copy-move regions. The proposed method accelerates block matching strategy. Firstly, the image is divided into fixed-size overlapping blocks then discrete cosine transform is applied to each block to represent its features. Fast k-means clustering technique is used to cluster the blocks into different classes. Zigzag scanning is performed to reduce the length of each block feature vector. The feature vectors of each cluster blocks are lexicographically sorted by radix sort, correlation between each nearby blocks indicates their similarity. The experimental results demonstrate that the proposed method can detect the duplicated regions efficiently, and reduce processing time up to 50% of other previous works.