Copy-move forgery detection for image forensics using the superpixel segmentation and the Helmert transformation

The increasing popularity of the internet suggests that digital multimedia has become easier to transmit and acquire more rapidly. This also means that this multimedia has become more susceptible to tampering through forgery. One type of forgery, known as copy-move duplication, is a specified type that usually involves image tampering. In this study, a keypoint-based image forensics approach based on a superpixel segmentation algorithm and Helmert transformation has been proposed. The purpose of this approach is to detect copy-move forgery images and to obtain forensic information. The procedure of the proposed approach consists of the following phases. First, we extract the keypoints and their descriptors by using a scale-invariant feature transform (SIFT) algorithm. Then, based on the descriptor, matching pairs will be obtained by calculating the similarity between keypoints. Next, we will group these matching pairs based on spatial distance and geometric constraints via Helmert transformation to obtain the coarse forgery regions. Then, we refine these coarse forgery regions and remove mistakes or isolated areas. Finally, the forgery regions can be localized more precisely. Our proposed approach is a more robust solution for scaling, rotation, and compression forgeries. The experimental results obtained from testing different datasets demonstrate that the proposed method can obtain impressive precision/recall rates in comparison to state-of-the-art methods.