Open Access

ARTICLE

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

Qi Cui^1,2,*, Suzanne McIntosh³, Huiyu Sun³

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

* Corresponding author: Qi Cui. Email: .

Computers, Materials & Continua 2018, 55(2), 229-241. https://doi.org/10.3970/cmc.2018.01693

Abstract

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%.

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Keywords

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