Sparsity-Based Image Inpainting Detection via Canonical Correlation Analysis With Low-Rank Constraints

Image inpainting, a commonly used image editing technique for filling the mask or missing areas in images, is often adopted to destroy the integrity of images by forgers with ulterior motives. Compared with the other types of inpainting, the sparsity-based inpainting exploits more general prior knowledge and has a broader application scope. Although many methods for detecting exemplar-based and diffusion-based inpainting have been successfully studied in the literature, there is still a lack of effective schemes for detecting the sparsity-based inpainting. In this paper, to fill this gap, we proposed a novel algorithm for sparsity-based image inpainting detection. We revealed the potential connection between sparsity-based inpainting and canonical correlation analysis (CCA). This type of inpainting has a strong effect on the CCA coefficients. Based on this observation, a modified objective function of CCA and a corresponding optimization algorithm are further proposed to enhance the inter-class difference in our feature set. Experimental results on three publicly available data sets demonstrated our method’s superiority over other competitors. Particularly, compared with previous inpainting detection methods, the proposed framework yields better performances in the cases of JPEG compression and Gaussian noise addition. The proposed method also shows promising results when employed to detect other types of inpainting.