Quaternion pseudo-Zernike moments combining both of RGB information and depth information for color image splicing detection

Document Type

Article

Publication Date

11-1-2017

Abstract

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.

Identifier

85030859199 (Scopus)

Publication Title

Journal of Visual Communication and Image Representation

External Full Text Location

https://doi.org/10.1016/j.jvcir.2017.08.011

e-ISSN

10959076

ISSN

10473203

First Page

283

Last Page

290

Volume

49

Grant

61232016

Fund Ref

National Natural Science Foundation of China

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