Abstract
Copy-Move Forgery Detection (CMFD) is a key issue of image forensics. A copy-move forgery is a type of image tampering that is created by copying a part of the image and pasting it on another part of the same image to perniciously hide or clone certain regions. This paper presents a new methodology for CMFD in digital images. The proposed algorithm is performed in two successive stages; matching stage and refinement stage. In the matching stage, close morphological operation and Connected Component Labeling (CCL) are used to segment the target image into different objects. The Speeded Up Robust Features (SURF) are extracted from each object and used to build an object catalog. The objects in the catalog are compared to each other, and matched objects are determined. If matched objects exist, the image is categorized as forged image. Otherwise, it is categorized as original image. The refinement stage, on the other hand, is implemented to ensure the originality of the target image. Thus, the candidate image that is classified as original is fed into the refinement stage to certify its originality. In this stage, close and open morphological operations as well as CCL are utilized to obtain the various objects in the image. Afterward, the SURF features are extracted from each object and used to build a new object catalog. The match between the objects in this catalog is obtained. If similar objects are found, the candidate image is classified as forged. Otherwise, the image is categorized as original. The proposed technique is assessed on four popular datasets. The results demonstrate the capability and robustness of the proposed technique in detecting the copy-move forgery under different geometrical attacks. Furthermore, the outcomes show that the suggested technique outperforms the previous CMFD methods in terms of Accuracy and execution time.