Image splicing forgery detection by combining synthetic adversarial networks and hybrid dense U‐net based on multiple spaces

International Journal of Intelligent SystemsVolume 37, Issue 11 p. 8291-8308 RESEARCH ARTICLE Image splicing forgery detection by combining synthetic adversarial networks and hybrid dense U-net based on multiple spaces Yang Wei, Yang Wei orcid.org/0000-0001-5068-1439 Shaanxi Key Laboratory of Network and System Security, School of Cyber Engineering, Xidian University, Xi'an, ChinaSearch for more papers by this authorJianfeng Ma, Jianfeng Ma Shaanxi Key Laboratory of Network and System Security, School of Cyber Engineering, Xidian University, Xi'an, ChinaSearch for more papers by this authorZhuzhu Wang, Corresponding Author Zhuzhu Wang zzwang_2@stu.xidian.edu.cn Shaanxi Key Laboratory of Network and System Security, School of Cyber Engineering, Xidian University, Xi'an, China Correspondence Zhuzhu Wang, Shaanxi Key Laboratory of Network and System Security, School of Cyber Engineering, Xidian University, No. 2 Southern Taibai Rd of Yanta District, 710071 Xi'an, Shaanxi, China. Email: zzwang_2@stu.xidian.edu.cn Bin Xiao, School of Computer Science and Technology, Chongqing University of Posts and Telecommunication, No. 2 Chongwen Rd of Nan'an District, Chongqing 400065, China. Email: xiaobin@cqupt.edu.cnSearch for more papers by this authorBin Xiao, Bin Xiao School of Computer Science and Technology, Chongqing University of Posts and Telecommunication, Chongqing, ChinaSearch for more papers by this authorWenying Zheng, Wenying Zheng College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaSearch for more papers by this author Yang Wei, Yang Wei orcid.org/0000-0001-5068-1439 Shaanxi Key Laboratory of Network and System Security, School of Cyber Engineering, Xidian University, Xi'an, ChinaSearch for more papers by this authorJianfeng Ma, Jianfeng Ma Shaanxi Key Laboratory of Network and System Security, School of Cyber Engineering, Xidian University, Xi'an, ChinaSearch for more papers by this authorZhuzhu Wang, Corresponding Author Zhuzhu Wang zzwang_2@stu.xidian.edu.cn Shaanxi Key Laboratory of Network and System Security, School of Cyber Engineering, Xidian University, Xi'an, China Correspondence Zhuzhu Wang, Shaanxi Key Laboratory of Network and System Security, School of Cyber Engineering, Xidian University, No. 2 Southern Taibai Rd of Yanta District, 710071 Xi'an, Shaanxi, China. Email: zzwang_2@stu.xidian.edu.cn Bin Xiao, School of Computer Science and Technology, Chongqing University of Posts and Telecommunication, No. 2 Chongwen Rd of Nan'an District, Chongqing 400065, China. Email: xiaobin@cqupt.edu.cnSearch for more papers by this authorBin Xiao, Bin Xiao School of Computer Science and Technology, Chongqing University of Posts and Telecommunication, Chongqing, ChinaSearch for more papers by this authorWenying Zheng, Wenying Zheng College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaSearch for more papers by this author First published: 14 June 2022 https://doi.org/10.1002/int.22939Read 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 With the popularity of image editing tools, the originality and information security of images are facing serious threats. The most common threat is splicing forgery that copies a part of the area from one donor image to the acceptor one. Some research works were proposed to protect the image originality, whereas they are still difficult to apply in practice. There are two main reasons: (a) very limited data for learning models; (b) huge attribute differences between the donor and acceptor images. We propose two novel tasks to conquer the above challenges: Synthetic Adversarial Networks (SANs) and Hybrid Dense U-Net (HDU-Net). SAN finds the most secluded position for inserting tampered areas in an image by learning the association between scenes and objects, and can enlarge the original small data set by more than 40 times. We call the data set SF-Data generated by SAN. We combine the dense U-Net that detects the differences of the essential attributes of image with four spaces containing more available feature information to propose HDU-Net. Then, the synthetic data set SF-Data are used to train HDU-Net. We perform various attack experiments on several public data sets to demonstrate the effectiveness and robustness of our method. Open Research DATA AVAILABILITY STATEMENT The data that support the findings of this study are available from the corresponding author upon reasonable request. Volume37, Issue11November 2022Pages 8291-8308 RelatedInformation