Geometric rectification‐based neural network architecture for image manipulation detection

International Journal of Intelligent SystemsVolume 36, Issue 12 p. 6993-7016 RESEARCH ARTICLE Geometric rectification-based neural network architecture for image manipulation detection Zhili Zhou, Corresponding Author Zhili Zhou zhou_zhili@163.com orcid.org/0000-0002-5641-7169 Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China Correspondence Zhili Zhou, Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, 210044 Nanjing, China. Email: zhou_zhili@163.comSearch for more papers by this authorWenyan Pan, Wenyan Pan orcid.org/0000-0003-0580-1950 Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, ChinaSearch for more papers by this authorQ. M. Jonathan Wu, Q. M. Jonathan Wu orcid.org/0000-0002-5208-7975 Department of Electrical and Computer Engineering, University of Windsor, Windsor, Ontario, CanadaSearch for more papers by this authorChing-Nung Yang, Ching-Nung Yang orcid.org/0000-0002-3881-7329 Department of Computer Science and Information Engineering, National Dong Hwa University, Hualian, TaiwanSearch for more papers by this authorZhihan Lv, Zhihan Lv orcid.org/0000-0003-2525-3074 School of Data Science and Software Engineering, Qingdao University, Qingdao, ChinaSearch for more papers by this author Zhili Zhou, Corresponding Author Zhili Zhou zhou_zhili@163.com orcid.org/0000-0002-5641-7169 Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China Correspondence Zhili Zhou, Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, 210044 Nanjing, China. Email: zhou_zhili@163.comSearch for more papers by this authorWenyan Pan, Wenyan Pan orcid.org/0000-0003-0580-1950 Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, ChinaSearch for more papers by this authorQ. M. Jonathan Wu, Q. M. Jonathan Wu orcid.org/0000-0002-5208-7975 Department of Electrical and Computer Engineering, University of Windsor, Windsor, Ontario, CanadaSearch for more papers by this authorChing-Nung Yang, Ching-Nung Yang orcid.org/0000-0002-3881-7329 Department of Computer Science and Information Engineering, National Dong Hwa University, Hualian, TaiwanSearch for more papers by this authorZhihan Lv, Zhihan Lv orcid.org/0000-0003-2525-3074 School of Data Science and Software Engineering, Qingdao University, Qingdao, ChinaSearch for more papers by this author First published: 26 July 2021 https://doi.org/10.1002/int.22577Citations: 2Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract Determination of image authenticity usually requires the identification and localization of the manipulated regions of images. Hence, image manipulation detection has become one of the most important tasks in the field of multimedia forensics. Recently, Convolutional Neural Networks (CNNs) have achieved promising performance in image manipulation detection. However, it is hard for the existing CNN-based manipulation detection approaches to accurately identify and localize the manipulated regions that have undergone geometric transformations, since CNNs are limited by their inability to be geometrically invariant. To address this issue, we propose a geometric rectification-based neural network architecture for image manipulation detection. In this type of network architecture, following the detection of a set of potential manipulated regions (PMRs) using Region Proposal Network, the Spatial Transformer Network is employed to geometrically rectify the convolutional feature maps (CFMs) of these regions to obtain the geometrically rectified CFMs (GR-CFMs). Subsequently, the residual feature maps (RFMs) are computed to capture the characteristic inconsistency between the CFMs and GR-CFMs of each PMR. Finally, the computed RFMs are automatically integrated with the GR-CFMs by a designed attention module to determine whether each PMR is a manipulated region and to localize the manipulated part at the pixel-level. Extensive experiments on the public data set as well as on our challenging data set demonstrate that the proposed network architecture achieves desirable performance in identifying and localizing regions with common tampering artifacts, which involve geometric transformations. Citing Literature Volume36, Issue12December 2021Pages 6993-7016 RelatedInformation