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231224s2017 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2017.2682963
|2 doi
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|a pubmed24n0900.xml
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|a DE-627
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Chen, Chenglong
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Blind Forensics of Successive Geometric Transformations in Digital Images Using Spectral Method
|b Theory and Applications
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| 264 |
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|c 2017
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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|a Date Completed 30.07.2018
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|a Date Revised 30.07.2018
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a Geometric transformations, such as resizing and rotation, are almost always needed when two or more images are spliced together to create convincing image forgeries. In recent years, researchers have developed many digital forensic techniques to identify these operations. Most previous works in this area focus on the analysis of images that have undergone single geometric transformations, e.g., resizing or rotation. In several recent works, researchers have addressed yet another practical and realistic situation: successive geometric transformations, e.g., repeated resizing, resizing-rotation, rotation-resizing, and repeated rotation. We will also concentrate on this topic in this paper. Specifically, we present an in-depth analysis in the frequency domain of the second-order statistics of the geometrically transformed images. We give an exact formulation of how the parameters of the first and second geometric transformations influence the appearance of periodic artifacts. The expected positions of characteristic resampling peaks are analytically derived. The theory developed here helps to address the gap left by previous works on this topic and is useful for image security and authentication, in particular, the forensics of geometric transformations in digital images. As an application of the developed theory, we present an effective method that allows one to distinguish between the aforementioned four different processing chains. The proposed method can further estimate all the geometric transformation parameters. This may provide useful clues for image forgery detection
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| 650 |
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|a Journal Article
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| 700 |
1 |
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|a Ni, Jiangqun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Shen, Zhaoyi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Shi, Yun Qing
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 26(2017), 6 vom: 01. Juni, Seite 2811-2824
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
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| 773 |
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|g volume:26
|g year:2017
|g number:6
|g day:01
|g month:06
|g pages:2811-2824
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| 856 |
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|u http://dx.doi.org/10.1109/TIP.2017.2682963
|3 Volltext
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