Abstract
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 part 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%.
Similar content being viewed by others
References
Lyu S, Farid H. How realistic is photorealistic? IEEE Trans Signal Process, 2005, 53(2–2): 845–850
Ng T T, Chang S F. Classifying photographic and photorealistic computer graphic images using natural image statistics. Technical Report. Columbia University, October 2004
Schaaf V D. Natural image statistics and visual processing. PhD thesis. The Netherlands: Rijksuniversiteit Groningen, 1998
Athitsos V, Swain M J, Franke C. Distinguishing photographs and graphics on the world wide web. Technical Report. Department of Computer Science, University of Chicago, 1997
Ng T T, Chang S F, Hsu J, et al. Physics-motivated features for distinguishing photographic images and computer graphics. In: Proc. of ACM Multimedia, 2005. 239–248
Ng T T, Chang S F. An online system for classifying computer graphics images from natural photographs. In: SPIE Electronic Imaging, San Jose, CA, January 2006
Dehnie S, Sencar T, Memon N. Digital image forensics for identifying computer generated and digital camera images. In: International Conference on Image Processing, 2006. 2313–2316
Farid H, Lyu S. Higher-order wavelet statistics and their application to digital forensics. In: IEEE Workshop on Statistical Analysis in Computer Vision, 2003
Ianeva T, De Vries A P, Rohrig H. Detecting cartoons: a case study in automatic video-genre classification. In: IEEE International Conference on Multimedia and Expo, 2003. I-449-52
Amanatides J. Realism in computer graphics: a survey. IEEE Comp Graph Appl, 1987, 7(1): 44–56
Kolb C, Mitchell D, Hanrahan P. A realistic camera model for computer graphics. In: Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques. 1995. 317–324
Falconer K. Fractal Geometry: Mathematical Foundations and Applications. New York: John Wiley & Sons, 1990
Chen W, Shi Y, Xuan G. Identifying computer graphics using HSV color model and statistical moments of characteristic functions. In: Multimedia and Expo, 2007 IEEE International Conference, 2007. 1123–1126
Smith A. Color gamut transform pairs. In: Proceedings of the 5th Annual Conference on Computer Graphics and Interactive Techniques, 1978. 12–19
Grassberger P. Generalized dimensions of strange attractors. Phys Lett, 1983, 97: 227–230
Chang C C, Lin C J. LIBSVM: a library for support vector machines, 2001
Author information
Authors and Affiliations
Corresponding author
Additional information
Supported by the National Natural Science Foundation of China (Grant Nos. 60633030 and 90604008), and National Basic Rearch Program of China (Grant No. 2006CB303104)
Rights and permissions
About this article
Cite this article
Pan, F., Chen, J. & Huang, J. Discriminating between photorealistic computer graphics and natural images using fractal geometry. Sci. China Ser. F-Inf. Sci. 52, 329–337 (2009). https://doi.org/10.1007/s11432-009-0053-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11432-009-0053-5