基于残差全卷积网络的图像拼接定位算法

doi:10.3969/j.issn.0255-8297.2019.05.007

应用科学学报 ›› 2019, Vol. 37 ›› Issue (5): 651-662.doi: 10.3969/j.issn.0255-8297.2019.05.007

基于残差全卷积网络的图像拼接定位算法

吴韵清¹, 吴鹏¹, 陈北京^1,2,3, 鞠兴旺¹, 高野¹

1. 南京信息工程大学计算机与软件学院, 南京 210044;
2. 南京信息工程大学江苏省大气环境与装备技术协同创新中心, 南京 210044;
3. 东南大学江苏省计算机网络技术重点实验室, 南京 210096

收稿日期:2019-07-27 修回日期:2019-08-01 出版日期:2019-09-30 发布日期:2019-10-18
通信作者: 陈北京,副教授,研究方向:数字取证、数字水印和模式识别,E-mail:nbutimage@126.com E-mail:nbutimage@126.com
基金资助:
国家自然科学基金（No.61572258，No.61772281，No.61602253）；江苏省大学生创新创业训练计划（No.201910300022Z）；江苏高校优势学科建设工程资助项目；江苏“高校青蓝工程”项目资助

Image Splicing Localization Method Based on Fully Convolutional Residual Networks

WU Yunqing¹, WU Peng¹, CHEN Beijing^1,2,3, JU Xingwang¹, GAO Ye¹

1. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China;
2. Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China;
3. Key Laboratory of Computer Network Technology of Jiangsu Province, Southeast University, Nanjing 210096, China

Received:2019-07-27 Revised:2019-08-01 Online:2019-09-30 Published:2019-10-18

摘要/Abstract

摘要： 为解决现有篡改定位网络随着深度加深不易收敛的问题，提出一种基于残差全卷积网络的图像拼接定位算法.所提算法一方面迁移残差思想，在全卷积神经网络（fullyconvolutional network，FCN）的部分卷积层中引入shortcut连接，使其输出的不仅是输入的映射，还是输入映射与输入的叠加.另一方面结合条件随机场（conditional random field，CRF）对定位结果进行后处理，并将FCN与CRF整合在一个端到端的学习系统中，进一步提高定位精度.此外，所提算法还融合3种FCN（FCN8、FCN16、FCN32）的预测结果.在实验中，随机选取公开数据集CASIA v2.0的5/6篡改图像作为训练集，然后对剩余1/6进行测试.为了测试提出算法的泛化性能，采用训练好的模型在公开数据集CASIA v1.0和DVMM上进行交叉测试.在3个数据集上的测试结果表明，所提算法的性能优于现有一些方法.

关键词: 拼接定位, 全卷积神经网络, 条件随机场, 残差网络

Abstract: In order to solve the problem that the existing forgery localization networks are not easy to converge with the increase of the depth of networks, an image splicing localization algorithm based on fully convolution residual networks is proposed in this paper. On the one hand, the proposed algorithm transfers the idea of residual structure and introduces shortcut connection into part of convolution layers in fully convolutional network (FCN), so that the output is not a mapping input alone but the superposition of a mapping input and an input itself. On the other hand, conditional random field (CRF) is used as post-processing operation to improve splicing localization accuracy. Moreover, FCN and CRF are integrated in an end-to-end learning system. In addition, the proposed algorithm combines the prediction results of three kinds of FCNs (FCN8, FCN16 and FCN32). In our experiment, 5/6 of the spliced images in the public available dataset CASIA v2.0 are randomly selected as training set and the rest are used for testing. In order to test generalization performance of the proposed algorithm, the trained model is also cross-tested on another two public available datasets CASIA v1.0 and DVMM. The overall test results on three datasets show that the proposed algorithm performs better than some existing algorithms.

Key words: splicing localization, fully convolutional network(FCN), conditional random field (CRF), ResNet

中图分类号:

TN911.22

吴韵清, 吴鹏, 陈北京, 鞠兴旺, 高野. 基于残差全卷积网络的图像拼接定位算法[J]. 应用科学学报, 2019, 37(5): 651-662.

WU Yunqing, WU Peng, CHEN Beijing, JU Xingwang, GAO Ye. Image Splicing Localization Method Based on Fully Convolutional Residual Networks[J]. Journal of Applied Sciences, 2019, 37(5): 651-662.

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基于残差全卷积网络的图像拼接定位算法

Image Splicing Localization Method Based on Fully Convolutional Residual Networks

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