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A detection of fake photos is a serious concern since general users can easily create with mobile apps and computer software. In this paper, a novel method that can detect fake photos accurately is proposed. RGB color model permutations are considered and non-decimated shift-invariant wavelet transform is applied. The proposed method extracts features using the Markov process and texture operator based on co-occurrence in both the spatial and frequency domains. The feature vector dimension is reduced by using an infinite feature selection algorithm and feature selection provides quality features to improve a detection accuracy and reduce a classification model training time. The experimental analysis is performed on four photo forgery datasets and demonstrated the accuracy of the proposed scheme is outstanding for both types of forgery, splicing and copy-move when compared with previous forgery detection schemes.

Photo forgery detection using RGB color model permutations