Orthogonal Matching Pursuit Guided Graph Sparse Representation in Image Tampering Detection and Visual Privacy Protection

Image tampering, a pervasive issue in the digital age, necessitates the development of robust detection techniques. This paper introduces a novel approach to image tampering detection by leveraging Orthogonal Matching Pursuit (OMP) to guide the construction of a graph-based sparse representation. The proposed method utilizes OMP to identify meaningful patterns within the image, which are then employed to construct a sparse graph representation. This graph representation offers an effective means of capturing the structural and contextual relationships among image elements. By comparing the sparse graph representation of an input image with that of reference authentic images, potential tampering can be detected through the identification of inconsistencies or irregularities. The approach's effectiveness is demonstrated through experimental results on a diverse set of tampered images, showcasing its ability to accurately discern tampering with a high degree of precision. This research method achieves accuracy rates of 96% for Image 1, 97% for Image 2, and an outstanding 98% for Image 3. The combination of OMP and graph-based sparse representation presents a promising avenue for image tampering detection, with applications in forensics and image authenticity verification.