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

Journal of Applied Sciences ›› 2019, Vol. 37 ›› Issue (5): 651-662.doi: 10.3969/j.issn.0255-8297.2019.05.007

• Special Issue: Information Security of Multimedia • Previous Articles Next Articles

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

Abstract

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

CLC Number:

TN911.22

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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Image Splicing Localization Method Based on Fully Convolutional Residual Networks

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