Image Inpainting Detection Based on a Modified Formulation of Canonical Correlation Analysis

Image inpainting is a common image editing technique for filling the missing areas in images. It can be adopted to destroy the integrity of images by forgers with ulterior motives. Compared with other types of inpainting, sparsity-based inpainting assumes more general prior knowledge and is more widely used in practical applications. Although several methods for detecting exemplar-based and diffusion-based inpainting have been proposed, there is a shortage of effective scheme for detecting sparsity-based inpainting. In this paper, we proposed a novel algorithm for sparsity-based image inpainting detection. This type of inpainting has a strong effect on the coefficients of Canonical Correlation Analysis (CCA). Based on this observation, a modified objective function of CCA and a corresponding optimization algorithm are further developed to enhance the difference of inter-class in our feature set. The experiments implemented on two publicly available datasets demonstrated our method's superiority over other competitors. Particularly, unlike previous inpainting detection methods, the proposed framework has better performance in the case of JPEG compression.