Journal of Computer Science and Technology ›› 2020, Vol. 35 ›› Issue (3): 592-602.doi: 10.1007/s11390-020-0216-9

Special Issue: Artificial Intelligence and Pattern Recognition; Computer Graphics and Multimedia

• Artificial Intelligence and Pattern Recognition • Previous Articles     Next Articles

Distinguishing Computer-Generated Images from Natural Images Using Channel and Pixel Correlation

Rui-Song Zhang1,2, Wei-Ze Quan1,2, Lu-Bin Fan3, Li-Ming Hu4, Dong-Ming Yan1,2,4,*, Senior Member, CCF, Member, ACM, IEEE        

  1. 1 National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;
    2 School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China;
    3 Alibaba Group, Hangzhou 310023, China;
    4 State Key Laboratory of Hydro-Science and Engineering, Tsinghua University, Beijing 100084, China
  • Received:2019-12-10 Revised:2020-01-05 Online:2020-05-28 Published:2020-05-28
  • Contact: Dong-Ming Yan E-mail:yandongming@gmail.com
  • About author:Rui-Song Zhang is currently pursuing his M.S. degree in the National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences, Beijing. He received his B.S. degree in information security from Xidian University, Xi'an, in 2019. His research interests include image processing and image forensics.
  • Supported by:
    This work was supported by the National Key Research and Development Program of China under Grant No. 2019YFB2204104, the Beijing Natural Science Foundation of China under Grant No. L182059, the National Natural Science Foundation of China under Grant Nos. 61772523, 61620106003, and 61802406, Alibaba Group through Alibaba Innovative Research Program, and the Joint Open Research Fund Program of State Key Laboratory of Hydroscience and Engineering and Tsinghua-Ningxia Yinchuan Joint Institute of Internet of Waters on Digital Water Governance.

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 system. In this work, we propose a convolutional neural network (CNN)-based model to distinguish computergenerated (CG) images from natural images (NIs) with channel and pixel correlation. The 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 explicitly. Unlike 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 networks. Experimental results demonstrate that the proposed network outperforms state-of-the-art methods in terms of classification performance. We also show that the newly introduced hybrid correlation module can improve the classification accuracy of different CNN architectures.

Key words: natural image; computer-generated image; channel and pixel correlation; convolutional neural network;

[1] He P, Jiang X, Sun T, Li H. Computer graphics identification combining convolutional and recurrent neural networks. IEEE Signal Processing Letters, 2018, 25(9):1369-1373.
[2] Ng T T, Chang S F, Hsu J, Xie L, Tsui M P. Physicsmotivated features for distinguishing photographic images and computer graphics. In Proc. the 13th ACM International Conference on Multimedia, November 2005, pp.239-248.
[3] Lyu S, Farid H. How realistic is photorealistic?IEEE Transactions on Signal Processing, 2005, 53(2):845-850.
[4] Chen W, Shi Y Q, Xuan G. Identifying computer graphics using HSV color model and statistical moments of characteristic functions. In Proc. the 2007 IEEE International Conference on Multimedia&Expo, July 2007, pp.1123-1126.
[5] Gallagher A C, Chen T. Image authentication by detecting traces of demosaicing. In Proc. the 2008 IEEE Conference on Computer Vision and Pattern Recognition, June 2008.
[6] Sankar G, Zhao H V, Yang Y H. Feature based classification of computer graphics and real images. In Proc. the IEEE International Conference on Acoustics, Speech and Signal Processing, April 2009, pp.1513-1516.
[7] Peng F, Zhou D l, Long M, Sun X M. Discrimination of natural images and computer generated graphics based on multi-fractal and regression analysis. AEU-International Journal of Electronics and Communications, 2017, 71:72-81.
[8] Rahmouni N, Nozick V, Yamagishi J, Echizen I. Distinguishing computer graphics from natural images using convolution neural networks. In Proc. the 2017 IEEE International Workshop on Information Forensics and Security, December 2017.
[9] Yu I, Kim D, Park J, Hou J, Choi S, Lee H. Identifying photorealistic computer graphics using convolutional neural networks. In Proc. the IEEE International Conference on Image Processing, September 2017, pp.4093-4097.
[10] Quan W, Wang K, Yan D M, Zhang X. Distinguishing between natural and computer-generated images using convolutional neural networks. IEEE Transactions on Information Forensics and Security, 2018, 13(11):2772-2787.
[11] Yao Y, Hu W, Zhang W, Wu T, Shi Y Q. Distinguishing computer-generated graphics from natural images based on sensor pattern noise and deep learning. Sensors, 2018, 18(4):Article No. 1296.
[12] Nguyen H H, Yamagishi J, Echizen I. Capsule-forensics:Using capsule networks to detect forged images and videos. In Proc. the 2019 IEEE International Conference on Acoustics, Speech and Signal Processing, May 2019, pp.2307-2311.
[13] Popescu A C, Farid H. Exposing digital forgeries by detecting traces of resampling. IEEE Transactions on Signal Processing, 2005, 53(2):758-767.
[14] Ianeva T I, de Vries A P, Röhrig H. Detecting cartoons:A case study in automatic video-genre classification. In Proc. the 2003 IEEE International Conference on Multimedia&Expo, July 2003, pp.449-452.
[15] Pan F, Chen J, Huang J. Discriminating between photorealistic computer graphics and natural images using fractal geometry. Science in China Series F:Information Sciences, 2009, 52(2):329-337.
[16] Zhang R, Wang R D, Ng T T. Distinguishing photographic images and photorealistic computer graphics using visual vocabulary on local image edges. In Proc. the 10th International Workshop on Digital Forensics and Watermarking, October 2012, pp.292-305.
[17] Li Z, Ye J, Shi Y Q. Distinguishing computer graphics from photographic images using local binary patterns. In Proc. the 11th International Workshop on Digital Forensics and Watermarking, October 2012, pp.228-241.
[18] Ojala T, Pietikäinen M, Maenpaa T. Multiresolution grayscale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7):971-987.
[19] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553):436-444.
[20] Zhang X J, Lu Y F, Zhang S H. Multi-task learning for food identification and analysis with deep convolutional neural networks. Journal of Computer Science and Technology, 2016, 31(3):489-500.
[21] Cheng M M, Hou Q B, Zhang S H, Rosin P L. Intelligent visual media processing:When graphics meets vision. Journal of Computer Science and Technology, 2017, 32(1):110-121.
[22] Quan W, Yan D M, Guo J, Meng W, Zhang X. Maximal Poisson-disk sampling via sampling radius optimization. In Proc. the SIGGRAPH ASIA, December 2016, Article No. 22.
[23] Barnes C, Zhang F L. A survey of the state-of-the-art in patch-based synthesis. Computational Visual Media, 2017, 3(1):3-20.
[24] Hinton G E, Krizhevsky A, Wang S D. Transforming autoencoders. In Proc. the 21st International Conference on Artificial Neural Networks, June 2011, pp.44-51.
[25] Sabour S, Frosst N, Hinton G E. Dynamic routing between capsules. In Proc. the 2017 Annual Conference on Neural Information Processing Systems, December 2017, pp.3856-3866.
[26] Tarianga B D, Senguptab P, Roy A, Chakraborty R S, Naskar R. Classification of computer generated and natural images based on efficient deep convolutional recurrent attention model. In Proc. the IEEE Conference on Computer Vision and Pattern Recognition Workshops, June 2019, pp.146-152.
[27] Yan Y, Ren W, Cao X. Recolored image detection via a deep discriminative model. IEEE Transactions on Information Forensics and Security, 2019, 14(1):5-17.
[28] Bottou L. Large-scale machine learning with stochastic gradient descent. In Proc. the 19th International Conference on Computational Statistics, August 2010, pp.177-186.
[29] Dang-Nguyen D T, Pasquini C, Conotter V, Boato G. RAISE:A raw images dataset for digital image forensics. In Proc. the 6th ACM Multimedia Systems Conference, March 2015, pp.219-224.
[30] Ng T T, Chang S F, Hsu J, Pepeljugoski M. Columbia photographic images and photorealistic computer graphics dataset. Technical Report, Department of Electrical Engineering, Columbia University, 2004. http://www.ee.columbia.edu/ln/dvmm/publications/05/ng cgdataset 05.pdf, Jan. 2019.
[31] Gunturk B K, Altunbasak Y, Mersereau R M. Color plane interpolation using alternating projections. IEEE Transactions on Image Processing, 2002, 11(9):997-1013.
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[4] Zhang Cui; Zhao Qinping; Xu Jiafu;. Kernel Language KLND[J]. , 1986, 1(3): 65 -79 .
[5] Wang Jianchao; Wei Daozheng;. An Effective Test Generation Algorithm for Combinational Circuits[J]. , 1986, 1(4): 1 -16 .
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