A Robust Seam Carving Forgery Detection Approach by Three-Element Joint Density of Difference Matrix
Seam carving is a popular content-aware image retargeting technique. However, it can also be used for malicious purposes such as object removal. In this paper, a robust blind forensics approach is proposed for seam-carved forgery detection. Since insignificant pixels along seams are removed for image resizing, the spatial neighborhood relations among pixels will be significantly changed, especially in smooth regions. Thus, joint density is exploited to model the change of spatially adjacent pixels’ distribution caused by seam carving, even in the case of low scaling ratios. Specifically, three-element joint density of difference matrix is computed to form general forensics features (GTJD). The GTJD features are combined with existing energy and noise features exacted in LBP domain for classification. Experimental results show that the proposed approach achieves better accuracies for both uncompressed images and JPEG images with different scaling ratios.
KeywordsBlind image forensic Content-aware image retargeting Seam carving Difference matrix Three-element joint density
This work is supported in part by the National Natural Science Foundation of China (61572183, 61379143, 61672222), the Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP) under grant 20120161110014, the Priority Academic Program Development of Jiangsu Higer Education Institutions (PAPD) and Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET).
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