Coarse-to-fine-grained method for image splicing region detection

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. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

The study aims to improve the accuracy of image splicing detection and proposes a progressive image splicing detection method. Features are extracted for coarse-grained detection, followed by fine-grained detection using texture strength features, and precise localization is achieved with an edge smoothing method. The proposed method demonstrates high detection accuracy and robustness against content-preserving manipulations and JPEG compression.