Block-based copy–move image forgery detection using DCT

Digital image forensic is a sub-area of multimedia security whose objective is to expose the malicious image manipulations in digital images. Among different types of image forgery, copy–move forgery is the most popular to forge the digital images where a part of the original digital image is copied and pasted at another position in the same image. Different methods have been developed to detect the image forgery in digital images. On the basis of our literature review, we identify that less attention is given to clustering algorithms to speed up the block matching strategy during image forgery detection process. Therefore, to address this issue, we present a pixel-based copy–move image forgery detection method to check the genuineness of digital images. Proposed method includes the following steps: (1) convert the color image into gray-scale image, (2) divide the gray-scale image into overlapping blocks of size 8 × 8, (3) feature extraction using DCT on the basis of different feature sets, (4) block clustering using K-means algorithm, and (5) radix sort for feature matching. Experimental results demonstrate that proposed method can efficiently detect the forged part from digital images.