Medical Image Forgery Detection By A Novel Segmentation Method With KPCA

Abstract

One of the most challenging tasks of recent days is detecting digital image forgery. Effective image processing tools are developed with excellent technology enhancement, making an easy and comfortable process for image forgery. Because of these systems’ misusage, the verification of picture is too difficult. Therefore, image forgery detection uses different techniques according to the requirements of detection, efficiency, and type of forgery. This study proposes an efficient novel segmentation method with Kernel Principal Component Analysis (KPCA) for the detection of image forgery. The Normalized cut (NCut) segmentation technique is used initially to segment the forged image. Then, the KPCA method is used to extract the image’s features. Finally, image authenticity is decided by comparison of clustering regions according to the threshold. In typical conditions of the image, as changes in brightness, shading diminishment, and limited rotation angles, the copied areas are detected accurately by using the described planned strategy. The performance of the proposed system is analyzed using different medical imaging modalities such as computed tomography, magnetic resonance imaging, x-ray and ultra-sonograms. From the comparative results, it is clear that better performance of image forgery detection is achieved by the described algorithm in terms of accuracy, precision, recall, and specificity than other algorithms.