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Identifying Materials of Photographic Images and Photorealistic Computer Generated Graphics Based on Deep CNNs

Qi Cui1,2,*, Suzanne McIntosh3, Huiyu Sun3

School of Computer and Software, Nanjing University of Information Science and Technology, Ning Liu Road, No. 219, Nanjing, 210044, China.
Jiangsu Engineering Centre of Network Monitoring, Ning Liu Road, No. 219, Nanjing, 210044, China.
Computer Science Department, New York University, New York, NY 10012, USA .

* Corresponding author: Qi Cui. Email: .

Computers, Materials & Continua 2018, 55(2), 229-241.


Currently, some photorealistic computer graphics are very similar to photographic images. Photorealistic computer generated graphics can be forged as photographic images, causing serious security problems. The aim of this work is to use a deep neural network to detect photographic images (PI) versus computer generated graphics (CG). In existing approaches, image feature classification is computationally intensive and fails to achieve real-time analysis. This paper presents an effective approach to automatically identify PI and CG based on deep convolutional neural networks (DCNNs). Compared with some existing methods, the proposed method achieves real-time forensic tasks by deepening the network structure. Experimental results show that this approach can effectively identify PI and CG with average detection accuracy of 98%.


Cite This Article

Q. . Cui, S. . McIntosh and H. . Sun, "Identifying materials of photographic images and photorealistic computer generated graphics based on deep cnns," Computers, Materials & Continua, vol. 55, no.2, pp. 229–241, 2018.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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