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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

    CMC-Computers, Materials & Continua, Vol.55, No.2, pp. 229-241, 2018, DOI: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… More >

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