Today’s Image processing tools have matured to a level where its users can effortlessly modify or enhance the images according to their requirement. A misuse of such tools has created a necessity for authenticating images to ensure its correctness. Image Forensics deals with the study of different kinds of manipulation on images and their detection. Image forgery detection algorithms detect forgery related artifacts which can be distinguished using specific image properties. Texture-based features have been widely used to detect forgery induced texture variations in the images. In this paper, we propose Region and Texture combined features for Image Forgery Detection. The Region-based approaches like – Edge-based Region Detection, Saliency-based Region Detection, and Wavelet-based Region Detection are captured, and on these regions, the texture feature- Rotation invariant Co-occurrences among adjacent LBP (RiCoLBP) is applied. The features thus obtained are optimized using Non-Negative Matrix Factorization and fed to a Support Vector Machine (SVM) for classification. The method is extensively evaluated on three benchmark datasets for image forgery detection namely CASIA v1.0, CASIA v2.0 and CUISDE. The performance reveals improved detection accuracies when compared to the state-of-the-art methods in detecting forged and authentic images.
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