Identifying natural images and computer-generated graphics based on convolutional neural network
by Min Long; Sai Long; Fei Peng; Xiao-hua Hu
International Journal of Autonomous and Adaptive Communications Systems (IJAACS), Vol. 14, No. 1/2, 2021

Abstract: Aiming at the identification of natural images and computer-generated graphics, an image source pipeline forensics method based on convolutional neural network (CNN) is proposed. In this method, Inception-v3 is used as the basic network, and the pre-trained model parameters in ImageNet are adopted. The top-level classification layer of Inception-v3 is replaced by two fully-connected Softmax classifiers. With the transfer learning, a new network model is constructed. The network is fine-tuned by a database with 10,000 images to identify natural images and computer-generated graphics. Experimental results and analysis show that it can effectively identify natural images and computer-generated graphics, and it is robustness against JPEG compression, scaling, rotation, noise and other post-processing operations. Furthermore, the effect of Softmax classifier and SVM classifier on the experimental results are analysed.

Online publication date: Thu, 15-Apr-2021

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