Journal
CMC-COMPUTERS MATERIALS & CONTINUA
Volume 65, Issue 1, Pages 929-943Publisher
TECH SCIENCE PRESS
DOI: 10.32604/cmc.2020.06569
Keywords
Median filtering forensics; quaternion convolution layer; quaternion pooling layer; color image
Funding
- Natural Science Foundation of China [61702235, 61772281, U1636219, U1636117, 61502241, 61272421, 61232016, 61402235, 61572258]
- National Key R\&D Program of China [2016YFB0801303, 2016QY 01W0105]
- plan for Scientific Talent of Henan Province [2018JR0018]
- Natural Science Foundation of Jiangsu Province, China [BK20141006]
- Natural Science Foundation of the Universities in Jiangsu Province [14KJB520024]
- PAPD fund
- CICAEET fund
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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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