TY - JOUR
T1 - Blind Forensics of Successive Geometric Transformations in Digital Images Using Spectral Method
T2 - Theory and Applications
AU - Chen, Chenglong
AU - Ni, Jiangqun
AU - Shen, Zhaoyi
AU - Shi, Yun Qing
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 61379156, in part by the National Research Foundation for the Doctoral Program of Higher Education of China under Grant 20120171110037, and in part by the Key Program of Natural Science Foundation of Guangdong under Grant S2012020011114.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/6
Y1 - 2017/6
N2 - 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.
AB - 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.
KW - Digital image forensics
KW - geometric transformations
KW - image forgery detection
KW - interpolation detection
KW - resampling detection
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U2 - 10.1109/TIP.2017.2682963
DO - 10.1109/TIP.2017.2682963
M3 - Article
AN - SCOPUS:85018774735
SN - 1057-7149
VL - 26
SP - 2811
EP - 2824
JO - IEEE Transactions on Image Processing
JF - IEEE Transactions on Image Processing
IS - 6
M1 - 7879296
ER -