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

• 作者: Huiyu Sun Qi Cui Suzanne McIntosh
• 来源期刊: 计算机、材料和连续体(英文)
• 学科: 工学
• 关键词: Image identification CNN DNN DCNNs COMPUTER GENERATED GRAPHICS
• 年，卷（期）: 2018,（5）
• 页码范围: 229-241
• 页数: 13页
• 分类号: TP3

摘要

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 realtime 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%.