TY - JOUR
T1 - Identifying Computer Generated Images Based on Quaternion Central Moments in Color Quaternion Wavelet Domain
AU - Wang, Jinwei
AU - Li, Ting
AU - Luo, Xiangyang
AU - Shi, Yun Qing
AU - Jha, Sunil Kr
N1 - Funding Information:
This work was supported in part by the Natural Science Foundation of China under Grant 61772281, Grant U1636219, Grant 61502241, Grant 61402235, and Grant 61572258, in part by the National Key R and D Program of China under Grant 2016YFB0801303 and Grant 2016QY01W0105, in part by the Plan for Scientific Talent of Henan Province under Grant 2018JR0018, in part by the Natural Science Foundation of Jiangsu Province under Grant BK20141006, in part by the Priority Academic Program Development of Jiangsu Higher Education Institutions, and in part by CICAEET. This paper was recommended by Associate Editor X. Cao.
Funding Information:
Manuscript received June 19, 2018; revised August 1, 2018; accepted August 18, 2018. Date of publication August 29, 2018; date of current version September 4, 2019. This work was supported in part by the Natural Science Foundation of China under Grant 61772281, Grant U1636219, Grant 61502241, Grant 61402235, and Grant 61572258, in part by the National Key R&D Program of China under Grant 2016YFB0801303 and Grant 2016QY01W0105, in part by the Plan for Scientific Talent of Henan Province under Grant 2018JR0018, in part by the Natural Science Foundation of Jiangsu Province under Grant BK20141006, in part by the Priority Academic Program Development of Jiangsu Higher Education Institutions, and in part by CICAEET. This paper was recommended by Associate Editor X. Cao. (Corresponding author: Xiangyang Luo.) J. Wang is with the Department of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China, and also with the State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou 450001, China (e-mail: wjwei@nuist.edu.cn).
Publisher Copyright:
© 1991-2012 IEEE.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019
Y1 - 2019
N2 - In this paper, a novel forensics scheme for color image is proposed in color quaternion wavelet transform (CQWT) domain. Compared with discrete wavelet transform (DWT), contourlet wavelet transform, and local binary patterns, CQWT processes a color image as a unit, and so, it can provide more forensics information to identify the photograph (PG) and computer generated (CG) images by considering the quaternion magnitude and phase measures. Meanwhile, two novel quaternion central moments for color images, i.e., quaternion skewness and kurtosis, are proposed to extract forensics features. In the condition of the same statistical model as Farid's model, the CQWT can boost the performance of the existing identification models. Compared with Farid's model and Li's model in 7500 PG and 7500 CG, the quaternion statistical features show a better classification performance. Results in the comparative experiments show that the classification accuracy of the CQWT improves by 19% more than Farid's model, and the quaternion features approximately improve by 2% more than the traditional.
AB - In this paper, a novel forensics scheme for color image is proposed in color quaternion wavelet transform (CQWT) domain. Compared with discrete wavelet transform (DWT), contourlet wavelet transform, and local binary patterns, CQWT processes a color image as a unit, and so, it can provide more forensics information to identify the photograph (PG) and computer generated (CG) images by considering the quaternion magnitude and phase measures. Meanwhile, two novel quaternion central moments for color images, i.e., quaternion skewness and kurtosis, are proposed to extract forensics features. In the condition of the same statistical model as Farid's model, the CQWT can boost the performance of the existing identification models. Compared with Farid's model and Li's model in 7500 PG and 7500 CG, the quaternion statistical features show a better classification performance. Results in the comparative experiments show that the classification accuracy of the CQWT improves by 19% more than Farid's model, and the quaternion features approximately improve by 2% more than the traditional.
KW - Color quaternion wavelet transform (CQWT)
KW - color image
KW - forensics
KW - quaternion feature
KW - quaternion statistics
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U2 - 10.1109/TCSVT.2018.2867786
DO - 10.1109/TCSVT.2018.2867786
M3 - Article
AN - SCOPUS:85052634491
SN - 1051-8215
VL - 29
SP - 2775
EP - 2785
JO - IEEE Transactions on Circuits and Systems for Video Technology
JF - IEEE Transactions on Circuits and Systems for Video Technology
IS - 9
M1 - 8450040
ER -