Color image splicing localization algorithm by quaternion fully convolutional networks and superpixel-enhanced pairwise conditional random field
Chen, Beijing; Gao, Ye; Xu, Lingzheng; Hong, Xiaopeng; Zheng, Yuhui; Shi, Yun-Qing (2019-07-29)
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
© 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0).
https://creativecommons.org/licenses/by/4.0/
https://urn.fi/URN:NBN:fi-fe202002286804
Tiivistelmä
Abstract
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.
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