Abstract
Due to the recent evolutions in the technologies various digital devices and image processing tools are available in the market. Consequently, crime rates are also proliferating in the developed and developing regions of the world. One such crime is the manipulation of digital image contents that can be achieved by using commercial and open-source image manipulation tools. The most widespread approach for image contents manipulation is the copy-move forgery. While crafting the copy-move forgeries (CMFs) it is often required to conceal undesired regions or duplicate desired regions in the image. Thus, forensic applications are needed to certify the contents of an image and to expose the manipulated areas. In this study, we are presenting a technique for detecting CMFs in digital images by image feature matching. The technique segments the suspected image into overlapping blocks and Tchebichef moments are computed for every block to characterize the manipulated regions of the image. Tchebichef moments are adopted because of their ability to represent image features more effectively as compared to other moments such as Legendre and Zernike moments. Each block of Tchebichef moments is further segmented into non-overlapping blocks and processed through singular values decomposition (SVD). To obtain a reduced size feature vector largest singular values of each sub-block are used that also enhanced the performance in the feature matching process. In the experiments standard databases namely, the DVMM Columbia University dataset, COVERAGE, and CoMoFoD are utilized to assess the performance of the proposed approach. The results conclusively demonstrate that the presented technique based on Tchebichef moments and SVD transcends the other methods.