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
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)
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.