Copy move image forgery detection using Hessian and center symmetric local binary pattern

Region duplication has become common in image forgery owing to the availability of advanced editing software and fully equipped digital cameras. Most existing block-based copy-move detection techniques struggle to detect such tampering under postprocessing operations, such as scaling and JPEG compression. This study proposes a copy-move image forgery detection algorithm using Hessian features and a center-symmetric local binary pattern (CSLBP). The proposed method consists of four steps: (1) detecting the object based on normalized cut segmentation, (2) localizing the local interest points of each object based on the Hessian method, (3) extracting CSLBP features, and (4) detecting duplicated regions in image forgeries. Experiment results show that the method is robust to postprocessed copy-move forgery under scaling, and JPEG compression.