Copy-Move Forgery Detection Using Color Space and Moment Invariants-Based Features

Due to the availability of different image processing tools, now-a-days Copy-move forgery is emerging immensely. Addressing this problem is being more deterrent because of different types of image post processing techniques. In this paper, we propose a robust approach to detect copy-move forgery detection which ensembles human vision information from color space of the image and moment invariants features to represent the objects of the images. Integration of color perception and object representation generates a powerful feature vector of reasonable size which ensures detection against different types of challenges. Experiments on benchmark dataset demonstrate the efficiency and robustness of the proposed approach on different image post processing methods.