A Quaternion Two-Stream R-CNN Network for Pixel-Level Color Image Splicing Localization

CHEN Beijing; JU Xingwang; GAO Ye; WANG Jinwei

doi:10.1049/cje.2021.08.004

Volume 30 Issue 6

Nov. 2021

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Article Navigation > Chinese Journal of Electronics > 2021 > 30(6): 1069-1079

CHEN Beijing, JU Xingwang, GAO Ye, et al., “A Quaternion Two-Stream R-CNN Network for Pixel-Level Color Image Splicing Localization,” Chinese Journal of Electronics, vol. 30, no. 6, pp. 1069-1079, 2021, doi: 10.1049/cje.2021.08.004

Citation:

CHEN Beijing, JU Xingwang, GAO Ye, et al., “A Quaternion Two-Stream R-CNN Network for Pixel-Level Color Image Splicing Localization,” Chinese Journal of Electronics, vol. 30, no. 6, pp. 1069-1079, 2021, doi: 10.1049/cje.2021.08.004

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A Quaternion Two-Stream R-CNN Network for Pixel-Level Color Image Splicing Localization

doi: 10.1049/cje.2021.08.004

CHEN Beijing^1,2,3,
JU Xingwang^1,2,
GAO Ye^1,2,
WANG Jinwei^{1,2
,
,}

1. Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science & Technology, Nanjing 210044, China;
2. School of Computer, Nanjing University of Information Science & Technology, Nanjing 210044, China;
3. Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing 210044, China

Funds:

This work is supported by the National Natural Science Foundation of China (No.62072251, No.62072250) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) Fund.

Received Date: 2021-04-14
Rev Recd Date: 2021-06-20

Available Online: 2021-09-23

Publish Date: 2021-11-05

Abstract

Abstract

Recently, Zhou et al. designed a twostream faster Region-Convolutional neural networks (RCNN) model RGB-N for color image splicing localization in CVPR2018. However, the RGB-N locates spliced regions only at block-level and ignores the entirety and inherent correlation of three channels. Therefore, an improved quaternion two-stream R-CNN model is proposed to solve these drawbacks:a mask branch combining fully convolutional network and condition random field is added for locating spliced regions at pixel-level; quaternion representation of color images is used to process color spliced images in a holistical way. In addition, feature pyramid network based on quaternion residual network is considered to extract multi-scale features for color spliced images; attention region proposal network is combined with attention mechanism and is designed to pay more attention to the spliced regions; a high-pass filter designed for image splicing detection specifically is adopted to replace steganalysis rich model filter in the RGB-N to obtain noise input for the noise stream. Experimental results on a new synthetic dataset and three standard forgery datasets demonstrate that the proposed method is superior to the existing methods in the abilities of localization, generalization, and robustness.
- Color image,
- Quaternion,
- Image splicing localization,
- R-CNN,
- ResNet

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References(41)

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