TY - JOUR
T1 - Quaternion pseudo-Zernike moments combining both of RGB information and depth information for color image splicing detection
AU - Chen, Beijing
AU - Qi, Xiaoming
AU - Sun, Xingming
AU - Shi, Yun Qing
N1 - Funding Information:
This work was supported by the NSFC under Grants 61572258 , 61232016 , 61572257 , and 61602253 , the Natural Science Foundation of Jiangsu Province of China under Grants BK20151530 , and BK20150925 , the PAPD fund , the China Scholarship Council , and sponsored by Qing Lan Project .
Publisher Copyright:
© 2017
PY - 2017/11
Y1 - 2017/11
N2 - The quaternion representation (QR) used in current quaternion-based color image processing creates redundancy when representing a color image of three components by a quaternion matrix having four components. In this paper, both RGB and depth (RGB-D) information are considered to improve QR for efficiently representing RGB-D images. The improved QR fully utilizes the four-dimensional quaternion domain. Using this improved QR, firstly we define the new quaternion pseudo-Zernike moments (NQPZMs) and then propose an efficient computational algorithm for NQPZMs through the conventional pseudo-Zernike moments (PZMs). Finally, we propose an algorithm for color image splicing detection based on the NQPZMs and the quaternion back-propagation neural network (QBPNN). Experimental results on four public datasets (DVMM, CASIA v1.0 and v2.0, Wild Web) demonstrate that the proposed splicing detection algorithm can achieve almost 100% accuracy with the appropriate feature dimensionality and outperforms 14 existing algorithms. Moreover, the comparison of six color spaces (RGB, HSI, HSV, YCbCr, YUV, and YIQ) shows that the proposed algorithm using YCbCr color space has the overall best performance in splicing detection.
AB - The quaternion representation (QR) used in current quaternion-based color image processing creates redundancy when representing a color image of three components by a quaternion matrix having four components. In this paper, both RGB and depth (RGB-D) information are considered to improve QR for efficiently representing RGB-D images. The improved QR fully utilizes the four-dimensional quaternion domain. Using this improved QR, firstly we define the new quaternion pseudo-Zernike moments (NQPZMs) and then propose an efficient computational algorithm for NQPZMs through the conventional pseudo-Zernike moments (PZMs). Finally, we propose an algorithm for color image splicing detection based on the NQPZMs and the quaternion back-propagation neural network (QBPNN). Experimental results on four public datasets (DVMM, CASIA v1.0 and v2.0, Wild Web) demonstrate that the proposed splicing detection algorithm can achieve almost 100% accuracy with the appropriate feature dimensionality and outperforms 14 existing algorithms. Moreover, the comparison of six color spaces (RGB, HSI, HSV, YCbCr, YUV, and YIQ) shows that the proposed algorithm using YCbCr color space has the overall best performance in splicing detection.
KW - Back-propagation neural network
KW - Depth information
KW - Pseudo-Zernike moment
KW - Quaternion
KW - Splicing detection
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U2 - 10.1016/j.jvcir.2017.08.011
DO - 10.1016/j.jvcir.2017.08.011
M3 - Article
AN - SCOPUS:85030859199
SN - 1047-3203
VL - 49
SP - 283
EP - 290
JO - Journal of Visual Communication and Image Representation
JF - Journal of Visual Communication and Image Representation
ER -