Open Access

ARTICLE

Median Filtering Detection Based on Quaternion Convolutional Neural Network

by Jinwei Wang, Qiye Ni, Yang Zhang, Xiangyang Luo, Yunqing Shi, Jiangtao Zhai, Sunil Kr Jha

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.

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Keywords

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