Vol.65, No.1, 2020, pp.929-943, doi:10.32604/cmc.2020.06569
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: xiangyangluo@126.com.
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
Median filtering forensics, quaternion convolution layer, quaternion pooling layer, color image.
Cite This Article
Wang, J., Ni, Q., Zhang, Y., Luo, X., Shi, Y. et al. (2020). Median Filtering Detection Based on Quaternion Convolutional Neural Network. CMC-Computers, Materials & Continua, 65(1), 929–943.
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