Rotation Invariant on Harris Interest Points for Exposing Image Region Duplication Forgery

Abstract

Nowadays, image forgery has become common because only an editing package soft- ware and a digital camera are required to counterfeit an image. Various fraud detection systems have been developed in accordance with the requirements of numerous applica- tions and to address different types of image forgery. However, image fraud detection is a complicated process given that is necessary to identify the image processing tools used to counterfeit an image. Here, we describe recent developments in image fraud detection. Conventional techniques for detecting duplication forgeries have difficulty in detecting postprocessing falsification, such as grading and joint photographic expert group com -pression. This study proposes an algorithm that detects image falsification on the basis of Hessian features. automatic reinstallation of duplicate areas hinders the practical applications of these algorithms. We propose a novel algorithm for the detection and description of scale and constant rotation in images. The algorithm is based on SURF and thus has powerful accel eration functions. SURF approximates or even exceeds the proposed thresholds for redun -dancy, excellence, and sustainability and rapidly performs calculation and comparison. This operation is performed by relying on image confluence. The exit detection and prescriptive prescriptions are based on their strengths (if a Hessian scale is used to detect and describe the established distribution), and kernel methods are simplified to allow the combination of new detection, description, and correspondence. Correspondence between two images of the same view and the objective is partly achieved by using many computers. In this study, pho -tography, three-dimensional reconstruction, image recording, and objective recoding were conducted. The search for a separate image match—the purpose of our research—can be separated into three principal steps. First, points of interest are specified in the characteristic locations of the image, such as angles, points, and plus T-intersections. The most important property of a detection method is its repeatability, that is, its reliability in finding similar indicators of interest under different conditions. Then, each point of interest is represented by a transmitter characteristic. This description must be distinct and must have similar time strengths under noise conditions, mistake detection, and geometrical and photometrical distortions. Finally, vector descriptors are adapted in different images. Correspondence is based on vector distance. Descriptor size directly affects computational time. Thus, fewer dimensions are desired. We aimed to develop an algorithm for the detection and the iden tification of fraud. We compared the performance of our proposed algorithm with that of a state-of-the-art detection algorithm. Our algorithm exhibits computational time and robust performance. Downsizing after description and complexity must be balanced while provid ing sufficient distinction. Various detection and description algorithms have been proposed in the literature (e.g., [1–3, 6, 7, 23]). Furthermore, detailed datasets for comparison and standard assessment have been established ]. We build upon the knowledge gained from previous work to better understand the aspects that contribute to algorithm performance.