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Title: Sparsity-based image inpainting detection via canonical correlation analysis with low-rank constraints
Authors: Jin, Xiao
Su, Yuting
Zou, Liang
Wang, Yongwei
Jing, Peiguang
Wang, Z. Jane
Keywords: Image Inpainting Detection
Image Forensics
DRNTU::Engineering::Computer science and engineering
Issue Date: 2018
Source: Jin, X., Su, Y., Zou, L., Wang, Y., Jing, P., & Wang, Z. J. (2018). Sparsity-based image inpainting detection via canonical correlation analysis with low-rank constraints. IEEE Access, 6, 49967-49978. doi:10.1109/ACCESS.2018.2866089
Series/Report no.: IEEE Access
Abstract: 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 other types of inpainting, 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 lack of effective schemes for detecting 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 datasets 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.
DOI: 10.1109/ACCESS.2018.2866089
Rights: © 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See for more information.
Fulltext Permission: open
Fulltext Availability: With Fulltext
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