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Detection of Content-Aware Image Resizing for Forensic Applications

Detection of Content-Aware Image Resizing for Forensic Applications

Guorui Sheng, Tiegang Gao, Shun Zhang
Copyright: © 2014 |Volume: 6 |Issue: 2 |Pages: 17
ISSN: 1941-6210|EISSN: 1941-6229|EISBN13: 9781466653481|DOI: 10.4018/ijdcf.2014040102
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MLA

Sheng, Guorui, et al. "Detection of Content-Aware Image Resizing for Forensic Applications." IJDCF vol.6, no.2 2014: pp.23-39. http://doi.org/10.4018/ijdcf.2014040102

APA

Sheng, G., Gao, T., & Zhang, S. (2014). Detection of Content-Aware Image Resizing for Forensic Applications. International Journal of Digital Crime and Forensics (IJDCF), 6(2), 23-39. http://doi.org/10.4018/ijdcf.2014040102

Chicago

Sheng, Guorui, Tiegang Gao, and Shun Zhang. "Detection of Content-Aware Image Resizing for Forensic Applications," International Journal of Digital Crime and Forensics (IJDCF) 6, no.2: 23-39. http://doi.org/10.4018/ijdcf.2014040102

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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

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