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Research on Novel natural image Reconstruction and Representation Algorithm based on Clustering and Modified Neural Network

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International Journal of Technology Management 2015年第10期 67-69页

作者： LU Dong-xing College of Electronic Information and Mechatronic Engineering ZhaoQing University Zhaoqing526061China

In this paper, we conduct research on the novel natural image reconstruction and representation algorithm based on clustenng and modified neural network. image resolution enhancement is one of the earliest researches of single image interpolation. Although the traditional interpolation and method for single image amplification is effect, but did not provide more useful information. Our method combines the neural network and the clustering approach. The experiment shows that our method performs well and satisfactory.

关键词： natural image Clustering Method Modified Neural Network image Representation.

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Distinguishing Computer-Generated images from natural images Using Channel and Pixel Correlation

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Journal of Computer Science & Technology 2020年第3期35卷 592-602页

作者： Rui-Song Zhang Wei-Ze Quan Lu-Bin Fan Li-Ming Hu Dong-Ming Yan National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Alibaba Group Hangzhou 310023China State Key Laboratory of Hydro-Science and Engineering Tsinghua UniversityBeijing 100084China

With the recent tremendous advances of computer graphics rendering and image editing technologies,computergenerated fake images,which in general do not reflect what happens in the reality,can now easily deceive the inspection of human visual *** this work,we propose a convolutional neural network(CNN)-based model to distinguish computergenerated(CG)images from natural images(NIs)with channel and pixel *** key component of the proposed CNN architecture is a self-coding module that takes the color images as input to extract the correlation between color channels *** previous approaches that directly apply CNN to solve this problem,we consider the generality of the network(or subnetwork),i.e.,the newly introduced hybrid correlation module can be directly combined with existing CNN models for enhancing the discrimination capacity of original *** results demonstrate that the proposed network outperforms state-of-the-art methods in terms of classification *** also show that the newly introduced hybrid correlation module can improve the classification accuracy of different CNN architectures.

关键词： natural image computer-generated image channel and pixel correlation convolutional neural network

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Discriminating between photorealistic computer graphics and natural images using fractal geometry

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Science in China(Series F) 2009年第2期52卷 329-337页

作者： PAN Feng CHEN JiongBin HUANG JiWu Guangdong Key Lab of Information Security Technology School of Information Science and Technology Sun Yat-Sen UniversityGuangzhou 510275 China

Rendering technology in computer graphics （CG） is now capable of producing highly photorealistic images, giving rise to the problem of how to identify CG images from natural images. Some methods were proposed to solve this problem. In this paper, we give a novel method from a new point of view of image perception. Although the photorealistic CG images are very similar to natural images, they are surrealistic and smoother than natural images, thus leading to the difference in perception. A pert of features are derived from fractal dimension to capture the difference in color perception between CG images and natural images, and several generalized dimensions are used as the rest features to capture difference in coarseness. The effect of these features is verified by experiments. The average accuracy is over 91.2%.

关键词： image forensics computer graphics natural image fractal image authentication

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Deep Learning for Distinguishing Computer Generated images and natural images:A Survey

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Journal of Information Hiding and Privacy Protection 2020年第2期2卷 95-105页

作者： Bingtao Hu Jinwei Wang Nanjing University of Information Science and Technology Nanjing210044China

With the development of computer graphics,realistic computer graphics(CG)have become more and more common in our field of *** rendered image is invisible to the naked *** to effectively identify CG and natural images(NI)has been become a new issue in the field of digital *** recent years,a series of deep learning network frameworks have shown great advantages in the field of images,which provides a good choice for us to solve this *** paper aims to track the latest developments and applications of deep learning in the field of CG and NI forensics in a timely ***,it introduces the background of deep learning and the knowledge of convolutional neural *** purpose is to understand the basic model structure of deep learning applications in the image field,and then outlines the mainstream framework;secondly,it briefly introduces the application of deep learning in CG and NI forensics,and finally points out the problems of deep learning in this field and the prospects for the future.

关键词： Deep learning convolutional neural network image forensics computer generated image natural image

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