Detection of Content-Aware Image Resizing for Forensic Applications

Detection of Content-Aware Image Resizing for Forensic Applications

Guorui Sheng (College of Information and Electric Engineering, LuDong University, YanTai, China), Tiegang Gao (College of Software, Nankai University, Tianjin, China) and Shun Zhang (College of Software, Nankai University, Tianjin, China)
Copyright: © 2014 |Pages: 17
DOI: 10.4018/ijdcf.2014040102
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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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With the fast development of digital image processing techniques, more and more image processing software like GIMP and Adobe Photoshop appeared and provided more and more powerful abilities to process digital images. As such software affording the convenience of processing digital image, they also generate the possibility of image tampering and misusing. If tampered images are used in the news media, scientific discovery, insurance and the court evidence, the social order and living order will be destroyed. False image change the true face of the world, even influence the judicial justice. Therefore, it becomes more and more important to confirm the authenticity of an image. The image forensics technique offers a favorable means for image tampering detection.

The image forensic techniques can be divided into two categories: proactive forensics and passive forensics. For proactive forensics, special information such as watermark is embedded into original image. When checking the authenticity of an image, such information will be extracted and compared with original embedded information to draw the conclusion. The main technique of proactive forensics is watermarking (Reyc, 2003; Potdar, 2005; Weng, 2013; Liu, 2010), which takes advantage of redundant information in digital images. Nowadays, some digital cameras support embedding watermark into digital image automatically when taking pictures. The main limitation of this kind of forensic is that, when facing large amounts of digital images, people usually can’t know which image should be checked in advance. On the contrary, the passive forensics can judge the authenticity of an image without any embedded information in advance. The wide application of this kind of technique motivated many researchers focus on this domain.

The main image tamping methods include copy-move, splicing, resampling and so on. Copy-move means copying one part of the original image and then pastes it to the other part of the same image. This kind of method is usually for the purpose of hiding or duplicating special contents such as a special object or people. One of the main corresponding forensic methods aiming at copy-move tamping is to find if there are homogeneous regions in the image, which can be achieved by using characteristics in frequency domain of the image (Fridrich, 2003; Amerini, 2011; Amerini, 2013). Image splicing means compounding two or more images into one, the corresponding forensic methods aiming at this respect are mainly achieved with the help of analysis of image features such as high order moment and bicoherence spectrum(Shi, 2007; Fu, 2006). Image resampling includes operations such as resizing, rotating and scaling. This kind of operation will resample the original image and cause periodic correlation which can be detected for forensic purpose.

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