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Classification of Computer Generated Images from Photographic Images Using Convolutional Neural Networks
Abstract:This paper presents a deep-learning mechanism for classifying computer generated images and photographic images. The proposed method accounts for a convolutional layer capable of automatically learning correlation between neighbouring pixels. In the current form, Convolutional Neural Network (CNN) will learn features based on an image's content instead of the structural features of the image. The layer is particularly designed to subdue an image's content and robustly learn the sensor pattern noise features (usually inherited from image processing in a camera) as well as the statistical properties of images. The paper was assessed on latest natural and computer generated images, and it was concluded that it performs better than the current state of the art methods.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1474745Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 786
 Bayar, B., Stamm, M.: A Deep Learning Approach to Universal Image Manipulation Detection Using A New Convolutional Layer.
 Rao, Y.; Ni, J.: A deep learning approach to detection of splicing and copy-move forgeries in images. In Proceedings of the IEEE International Workshop on Information Forensics and Security (WIFS), Abu Dhabi, UAE, 4–7 December 2016; 1-6.
 Rahmouni, N., Nozick, V., Yamagishi, J., Echizen, I.: Distinguishing Computer Graphics from Natural Images Using Convolution Neural Networks. In Proceedings of the IEEE International Workshop on Information Forensics and Security (WIFS), Rennes, France, December 4-7, 2017; 1-6.
 Tuama, A., Comby, F., Chaumont, M.: Camera model identiﬁcation with the use of deep convolutional neural networks. In Proceedings of the IEEE International Workshop on Information Forensics and Security (WIFS), Abu Dhabi, UAE, 4–7 December 2016; 1–6
 Xu, G., Wu, H.Z., Shi, Y.Q.: Structural Design of Convolutional Neural Networks for Steganalysis. IEEE Signal Processing Letters 2016, 23, 708–712.
 Yao, Y., Shi, Y., Weng, S.: Guan, B. Deep Learning for Detection of Object-Based Forgery in Advanced Video. Symmetry 2018, 10, 1-10.
 Piaskiewicz, M.: Level-design reference database. (Online). Available: http://level-design.org/referencedb/.
 Dang-Nguyen, D.-T. Pasquini, C., Conotter, V. and Boato, G.:Raise: a raw images dataset for digital image forensics, in Proceedings of the 6th ACM Multimedia Systems Conference. ACM, 2015, pp. 219–224.
 Farid H. and Lyu, Su.: Higher-order wavelet statistics and their application to digital forensics, in IEEE Workshop on Statistical Analysis in Computer Vision, Madison, Wisconsin, 2003, p. 94.
 Ng T.-T and Chang, S.-F: An online system for classifying computer graphics images from natural photographs, in Electronic Imaging 2006. International Society for Optics and Photonics, 2006, pp. 607 211– 607 211.
 Dirik, A. E, Bayram, S., Sencar, H. T and Memon N.: New features to identify computer generated images, in IEEE International Conference on Image Processing, ICIP 2007, vol. 4. IEEE, 2007, pp. IV–433.
 Wang, R. Fan, S. and. Zhang, Y.: Classifying computer generated graphics and natural imaged based on image contour information, J. Inf. Comput.
 Wang, Y. and Moulin, P.: On discrimination between photorealistic and photographic images, in Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on, vol. 2. IEEE, 2006, pp. II–II.
 Bondi, L., Barofﬁo, L., G¨uera, D., Bestagini, P. Delp, E. J.: Tubaro, S. First Steps Toward Camera Model Identiﬁcation With Convolutional Neural Networks. IEEE Signal Processing Letters 2017, 24, 259–263.
 Gando, G., Yamada, T., Sato, H., Oyama, S.: Kurihara, M. Fine-tuning deep convolutional neural networks for distinguishing illustrations from photographs. Expert Systems with Applications 2016, 66, 295–301.
 Rocha, A., Scheirer, W., Boult, T., Goldenstein, S.: Vision of the unseen Current trends and challenges in digital image and video forensics. ACM Computing Surveys 2011, 43, 26–40.
 Krizhevsky, A, Sutskever, I, and Hinton, G. E: Imagenet classiﬁcation with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097–1105, 2012.
 Nair, V, Hinton, G. E.: Rectiﬁed linear units improve restricted boltzmann machines. In International Conference on Machine Learning, pages 807–814, 2010.