@Article{cmc.2020.06569,
AUTHOR = {Jinwei Wang, Qiye Ni, Yang Zhang, Xiangyang Luo, Yunqing Shi, Jiangtao Zhai, Sunil Kr Jha},
TITLE = {Median Filtering Detection Based on Quaternion Convolutional  Neural Network},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {1},
PAGES = {929--943},
URL = {http://www.techscience.com/cmc/v65n1/39604},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.06569}
}