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

Document Type

Article

Publication Date

1-1-2019

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.

Identifier

85070741621 (Scopus)

Publication Title

Mathematical Biosciences and Engineering

External Full Text Location

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

e-ISSN

15510018

ISSN

15471063

PubMed ID

31698595

First Page

6907

Last Page

6922

Issue

6

Volume

16

Grant

61572258

Fund Ref

National Natural Science Foundation of China

This document is currently not available here.

Share

COinS