Color image splicing localization algorithm by quaternion fully convolutional networks and superpixel-enhanced pairwise conditional random field

Recently, fully convolutional network (FCN) has been successfully used to locate spliced regions in synthesized images. However, all the existing FCN-based algorithms use real-valued FCN to process each channel separately. As a consequence, they fail to capture the inherent correlation between color channels and the integrity of three channels. So, in this paper, quaternion fully convolutional network (QFCN) is proposed to generalize FCN to quaternion domain by replacing real-valued conventional blocks in FCN with quaternion conventional blocks. In addition, a new color image splicing localization algorithm is proposed by combining QFCNs and superpixel (SP)-enhanced pairwise conditional random field (CRF). QFCNs consider three different versions (QFCN32, QFCN16, and QFCN8) with different up-sampling layers. The SP-enhanced pairwise CRF is used to refine the results of QFCNs. Experimental results on three publicly available datasets demonstrate that the proposed algorithm outperforms the existing algorithms including some conventional algorithms and some deep learning-based algorithms.

Authors:
Chen Beijing, Gao Ye, Xu Lingzheng, Hong Xiaopeng, Zheng Yuhui, Shi Yun-Qing

Publication type:
A1 Journal article – refereed

Place of publication:

Keywords:
conditional random field, fully convolutional network, quaternion, splicing detection, splicing localization

Published:

Full citation:
Beijing Chen, Ye Gao, Lingzheng Xu, Xiaopeng Hong, Yuhui Zheng, Yun-Qing Shi. Color image splicing localization algorithm by quaternion fully convolutional networks and superpixel-enhanced pairwise conditional random field. Mathematical Biosciences and Engineering, 2019, 16(6): 6907-6922. doi: 10.3934/mbe.2019346

DOI:
https://doi.org/10.3934/mbe.2019346

Read the publication here:
http://urn.fi/urn:nbn:fi-fe202002286804