Coarse-to-fine-grained method for image splicing region detection. (February 2022)

• Record Type: Journal Article
• Authors: Wang, Xiaofeng; Wang, Yan; Lei, Jinjin; Li, Bin; Wang, Qin; Xue, Jianru
• Is Part Of: Pattern recognition. Volume 122(2022)
• Publication Date: 2022-02
• Subjects: Image splicing detection -- CFA interpolation algorithm -- Forensics features -- Texture strength features -- Edges smoothing; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2021.108347
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: Interpreting the difference of CFA interpolation pattern as an evidence of splicing. Apply the forensics features to obtain the coarse-grained detection results. Use local texture strength features to perform fine-grained detection. Use a method named edge smoothing to realize precise localization. Abstract: In this study, we aim to improve the accuracy of image splicing detection. We propose a progressive image splicing detection method that can detect the position and shape of spliced region. Because image splicing is likely to destroy or change the consistent correlation pattern introduced by color filter array (CFA) interpolation process, we first used a covariance matrix to reconstruct the R, G and B channels of image and utilized the inconsistencies of the CFA interpolation pattern to extract forensics feature. Then, these forensics features were used to perform coarse-grained detection, and texture strength features were used to perform fine-grained detection. Finally, an edge smoothing method was applied to realize precise localization. As compared to the state-of-the-art CFA-based image splicing detection methods, the proposed method has a high-level detection accuracy and strong robustness against content-preserving manipulations and JPEG compression.