Abstract
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.
Supported by Program for Young Innovative Research Team in Shandong University of Political Science and Law(Network Information Security and Forensics, Intelligent Information Processing);Big data and Artificial Intelligence Legal Research Collaborative Innovation Center of Shandong University of Political Science and Law; Key Laboratory of Evidence-Identifying in Universities of Shandong (SDUPSL); Projects of Shandong Province Higher Educational Science and Technology Program under Grant No.J16LN19, J18KA357, J18KA383; Projects of Shandong University of Political Science and Law under Grant No. 2016Z03B, 2015Z03B,2019KQ13Z.
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Yang, T., Wu, J., Feng, G., Chang, X., Liu, L. (2020). A Deep Learning Approach to Detection of Warping Forgery in Images. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Lecture Notes in Computer Science(), vol 12240. Springer, Cham. https://doi.org/10.1007/978-3-030-57881-7_10
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