Abstract
ABSTRACT The majority of the KP-based copy-move forgery (CMF) detection techniques have a high computational cost due to their large feature descriptor sets and numerous KPs. Furthermore, the accuracy of the results may be impacted by the identified KPs not spreading over all regions of the image and the classical clustering approaches not efficiently classifying the feature space into cluster space. This article therefore attempts to employ both KAZE & fast hessian matrix (FHM) techniques for identifying KPs that spread over the entire image region, SURF for evaluating feature descriptors, Network based Dimensionality Reduction (NDR) for reducing the dimension of each feature descriptor and Self-Organizing Map (SOM) for clustering the feature vectors for avoiding sub-optimal clusters. It portrays the superior performances of the proposed forgery detection scheme on a standard image database “MICC-F220” and medical records like fundas images.
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