标题:A Deep Learning Approach to Detection of Warping Forgery in Images
作者:Yang, Tongfeng ;Wu, Jian ;Feng, Guorui ;Chang, Xu ;Liu, Lihua
通讯作者:Yang, Tongfeng
作者机构:[Yang, Tongfeng ;Wu, Jian ;Feng, Guorui ;Chang, Xu ;Liu, Lihua ] Shandong University of Political Science and Law, Jinan; Shandong, China
会议名称:6th International Conference on Artificial Intelligence and Security, ICAIS 2020
会议日期:17 July 2020 through 20 July 2020
来源:Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
出版年:2020
卷:12240 LNCS
页码:109-118
DOI:10.1007/978-3-030-57881-7_10
关键词:Convolutional neural networks; Image forensics; Image warping
摘要:In recent years, image forensics has received full attention from researchers. A large number of algorithms for image smoothing, JPEG compression, copy-move, and shear tampering were published. However, there are still many image tampering algorithms that are not involved. In this paper, we publish a dataset of image warping, which contains more than 10000 images, and propose a novel convolutional neural network called DWF-CNN to identify warped images. In experiments, we compared the performance with 4 alternative networks. The proposed network with the preprocessing layer of the SRM layer and Bayar convolutional layer got the best result, which reached to the accuracy of 99.36%. The experiments also showed that the network with the regular convolutional layer performed even worse than a random guess. It illustrates the importance of the well-designed preprocessing layer in this research area again. © 2020, Springer Nature Switzerland AG.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091266948&doi=10.1007%2f978-3-030-57881-7_10&partnerID=40&md5=44fe15249bc973db670eb793b29f9f89
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