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Detection of Content-Aware Image Resizing for Forensic Applications

Guorui Sheng, Tiegang Gao and Shun Zhang
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Guorui Sheng: College of Information and Electric Engineering, LuDong University, YanTai, China
Tiegang Gao: College of Software, Nankai University, Tianjin, China
Shun Zhang: College of Software, Nankai University, Tianjin, China

International Journal of Digital Crime and Forensics (IJDCF), 2014, vol. 6, issue 2, 23-39

Abstract: Seam-Carving is widely used for content-Aware image resizing. To cope with the digital image forgery caused by Seam-Carving, a new detecting algorithm based on Expanded Markov Feature (EMF) is presented. The algorithm takes full advantage of Transition Probability Matrix to represent correlation change caused by Seam-Carving operation. Different with traditional Markov features, the EMF not only reflects the change of correlation within the intra-DCT block, it also represents the change of correlation in more extensive range. The EMF is a fusion of traditional and expanded Markov Transition Probability Matrix. In the proposed algorithm, The EMF of normal image and that of forged image is trained by SVM, and thus the nornal image and forged image by Seam-Carving can be discriminated by SVM. The experimental result shows that the performance of proposed method is better than that of the method based on traditional Markov features and other existing methods

Date: 2014
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