Abstract
Image forgery detection is one of the prominent areas from research and development perspective. This research work aims to propose a scheme for the detection of multiple types of image forgeries. In this paper, a generic passive image forgery scheme is proposed using spatial rich model (SRM) in combination with textural feature i.e. local binary pattern (LBP). Moreover, different sub-model selection strategies are implemented and analyzed to investigate the performance-to-model dimensionality trade-off. Ensemble multi-class classifier is used for classifying the features into different forgery classes. The proposed scheme is evaluated on the dataset generated from IEEE IFS-TC image forensics challenge containing 10 different kinds of forgeries. The results reveal that computing LBP on noise residuals in conjunction with co-occurrence matrices using BEST-q-CLASS feature selection strategy produces a model which performs efficiently for almost any set of modifications with accuracy of 98.4%.
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