Papers › Identifying materials of photographic images and photorealistic computer generated...
Identifying materials of photographic images and photorealistic computer generated graphics based on deep CNNs
Qi Cui, Suzanne McIntosh, Huiyu Sun
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 an average detection accuracy of 98%.
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