Open Access
ARTICLE
Median Filtering Detection Based on Quaternion Convolutional Neural Network
Jinwei Wang1, 2, 3, 4, Qiye Ni3, Yang Zhang3, Xiangyang Luo2, *, Yunqing Shi5, Jiangtao Zhai3, Sunil Kr Jha3
1 Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing, 210044, China.
2 State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, 450001, China.
3 Department of Computer and Software, Nanjing University of Information Science & Technology, Nanjing, 210044, China.
4 State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100093, China.
5 Department of Electrical Computer Engineering, New Jersey Institute of Technology, New Jersey, NJ07102, USA.
* Corresponding Author: Xiangyang Luo. Email: .
Computers, Materials & Continua 2020, 65(1), 929-943. https://doi.org/10.32604/cmc.2020.06569
Received 08 March 2019; Accepted 28 March 2019; Issue published 23 July 2020
Abstract
Median filtering is a nonlinear signal processing technique and has an
advantage in the field of image anti-forensics. Therefore, more attention has been paid to
the forensics research of median filtering. In this paper, a median filtering forensics
method based on quaternion convolutional neural network (QCNN) is proposed. The
median filtering residuals (MFR) are used to preprocess the images. Then the output of
MFR is expanded to four channels and used as the input of QCNN. In QCNN, quaternion
convolution is designed that can better mix the information of different channels than
traditional methods. The quaternion pooling layer is designed to evaluate the result of
quaternion convolution. QCNN is proposed to features well combine the three-channel
information of color image and fully extract forensics features. Experiments show that
the proposed method has higher accuracy and shorter training time than the traditional
convolutional neural network with the same convolution depth.
Keywords
Cite This Article
J. Wang, Q. Ni, Y. Zhang, X. Luo, Y. Shi
et al., "Median filtering detection based on quaternion convolutional neural network,"
Computers, Materials & Continua, vol. 65, no.1, pp. 929–943, 2020.