Open AccessA Quaternion Two‐Stream R‐CNN Network for Pixel‐Level Color Image Splicing Localization
Author(s) -
Beijing CHEN,
Xingwang JU,
Ye GAO,
Jinwei WANG
Publication year - 2021
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2021.08.004
Subject(s) - artificial intelligence , rgb color model , computer science , quaternion , convolutional neural network , pattern recognition (psychology) , computer vision , robustness (evolution) , pixel , color image , mathematics , image processing , image (mathematics) , biochemistry , chemistry , geometry , gene
Recently, Zhou et al . designed a two‐stream faster Region‐Convolutional neural networks (R‐CNN) 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.