Abstract
This article describes a mathematical method to distinguish computer graphics (CG) from photographic images (PG). Because white balance, CFA and PRNU noise artifacts are the intrinsic properties of optical imaging, we can use these artifacts to reflect camera imaging-specific to some extent. During experiment, we design a 135-D feature set and perform distinguishing process to capture these artifacts. Images selected from relative Columbia University image database are chosen as our experiment database. Experiment result indicates our mathematical method is capable to identify computer generated images from camera produced images with 95.43 % accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lyu, S., Farid, H.: How Realistic is Photorealistic? IEEE Trans. On Signal Processing 53(2), 845–850 (2005)
Ng, T.-T., Chang, S.-F., Hsu, J., Xie, L., Tsui, M.-P.: Physics-motivated features for distinguishing photographic images and computer graphics. In: ACM Multimedia (2005)
Wang, Y., Moulin, P.: On discrimination between photorealistic and photographic images [corrected version]. In: Proceedings of IEEE ICASSP (2006)
Dehnie, S., Sencar, H.T., Memon, N.: Digital image forensics for identifying computer generated and digital camera images. In: IEEE International Conference on Image Processing, pp. 2313–2316 (2006)
Dirik, A.E., Bayram, S., Sencar, H.T., Memon, N.: New features to identify computer generated images. In: Proceedings of IEEE ICIP (2007)
Chen, W., Shi, Y.Q., Xuan, G.: Identifying computer graphics using HSV color model and statistical moments of characteristic functions. IEEE International Conference on Multimedia and Expo (ICME07), Beijing, China, 2–5 July 2007
Khanna, N., Chiu, G.T.-C., Allebach, J.P., Delp, E.J.: Forensic techniques for classifying scanner, computer generated and digital camera images. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 1653–1656 (2008)
Sutthiwan, P., Cai, X., Shi, Y.Q., Zhang, H.: Computer graphics classification based on Markov process model and boosting feature selection technique. In: IEEE International Conference on Image Processing, pp. 2913–2916 (2009)
Conotter, V., Cordin, L.: Detecting photographic and computer generated composites. In: Proceedings of SPIE 7870, Image Processing: Algorithms and Systems IX, 78700A, 03 February 2011
Farid, H., Bravo, M.J.: Perceptual discrimination of computer generated and photographic faces. Digital Invest. 8(3–4), 226–235 (2012)
Min, H., Jin, C.H., You S.-H.: A neural network approach to color constancy (ICCAS 2011). In: 2011 11th International Conference on Control, Automation and Systems (ICCAS), pp. 1678–1681, 26–29 Oct 2011
Kloss, G.K.: Colour constancy using von Kries transformations - colour constancy “goesto the Lab”. Res. Lett. Inf. Math. Sci. 2009(13), 19–33 (2009)
Horé, A., Ziou, D.: An edge-sensing universal demosaicing algorithm. In: Blanc-Talon, J., Bone, D., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2010, Part I. LNCS, vol. 6474, pp. 197–208. Springer, Heidelberg (2010)
Magiera, P., Löndahl, C.: ROF denoising algorithm (2008). http://www.mathworks.com/matlabcentral/fileexchange/22410-rof-denoising-algorithm/content/ROFdenoise.m
http://www.ee.columbia.edu/ln/dvmm/downloads/PIM_PRCG_dataset/
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2012). http://www.csie.ntu.edu.tw/~cjlin/libsvm/
Acknowledgements
This work is supported in part by: NSF (61272278), Central University special (111087),The major national science and technology special projects (2010ZX03004-003-03), National Nature Science Foundation of China (No. 60832002), Nature Science Foundation of Hubei Province (No. 2009CDB222, No. 2010CDB08602), and Doctoral Fund of Ministry of Education of China (Grant No.200910141110054), E-learning system product development and application, Fundamental Research Funds for the Central Universities (3105005), New mobile multimedia audio and video codec key technology research and development, National level Quality-Engineering Excellent Courses: Computer System and Interface, New Generation broadband wireless mobile information network.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gao, S., Zhang, C., Wu, CL., Ye, G., Huang, L. (2014). A Hybrid Feature Based Method for Distinguishing Computer Graphics and Photo-Graphic Image. In: Shi, Y., Kim, HJ., Pérez-González, F. (eds) Digital-Forensics and Watermarking. IWDW 2013. Lecture Notes in Computer Science(), vol 8389. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43886-2_22
Download citation
DOI: https://doi.org/10.1007/978-3-662-43886-2_22
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-43885-5
Online ISBN: 978-3-662-43886-2
eBook Packages: Computer ScienceComputer Science (R0)