Identification of Natural Images and Computer Generated Graphics Based on Multiple LBPs in Multicolor Spaces
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In this paper, a digital image forensics scheme based on multiple local binary patterns (LBP) in multicolor spaces is proposed for distinguishing natural images (NI) from computer generated graphics (CG). Based on the fact that the texture of natural images is more complex than that of the computer generated graphics, the diversity of their texture features are used for identification. But typical LBP only consider a single color space and a single color channel, it cannot represent the differences between NI and CG exactly. Here, we introduced two new LBP descriptors called median robust extended local binary pattern (MRELBP) and chromatic co-occurrence of adjacent local binary patterns (CCoALBP) to extract features for image identification. In our method, features of these two LBP descriptors are cascaded and they are used as the input of SVM classifier. Experimental results and analysis indicate that it can effectively identify NI and CG, and it also has a good robustness against JPEG compression, resizing, rotation and adding noise.
KeywordsDigital image forensics Texture feature Natural images Computer generated graphics Local binary patterns
This work was supported in part by project supported by National Natural Science Foundation of China (Grant Nos. 61572182, 61370225), project supported by Hunan Provincial Natural Science Foundation of China (Grant No. 15JJ2007).
- 1.Bayram, S., Sencar, H., Memon, N., Avcibas, I.: Source camera identification based on CFA interpolation. In: IEEE International Conference on Image Processing 2005, vol. 3, pp. III-69–72 (2005)Google Scholar
- 4.Nosaka, R., Ohkawa, Y., Fukui, K.: Feature extraction based on co-occurrence of adjacent local binary patterns. In: Proceedings of the 5th Pacific Rim Conference on Advances in Image and Video Technology, pp. 82–91 (2012)Google Scholar
- 7.Lv, Y., Wan, G., Shen, X.J., Chen, H.P.: Blind identification of photorealistic computer graphics based on fractal dimensions. In: Proceedings of the 2014 International Conference on Computer, Communications and Information Technology, pp. 257–260 (2014)Google Scholar
- 10.Gallagher, A.C., Chen, T.: Image authentication by detecting traces of demosaicing. In: 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR workshops), pp. 1–8 (2008)Google Scholar
- 12.Sutthiwan, P., Cai, X., Shi, Y., Zhang, H.: Computer graphics classification based on markov process model and boosting feature selection technique. In: Proceedings of the 2009 16th IEEE International Conference on Image Processing (ICIP 2009), pp. 2913–2916 (2009)Google Scholar
- 14.Zhu, C., Bichot, C.-E., Chen, L.: Multi-scale color local binary patterns for visual object classes recognition. In: Proceedings of the 2010 20th International Conference on Pattern Recognition (ICPR 2010), pp. 3065–3068 (2010)Google Scholar
- 16.Ng, T.T., Chang, S.F., Hsu, J., Pepeljugoski, M.: Columbia photographic images and photorealistic computer graphics dataset, ADVENT. Columbia University, Technical report, 205-2004-5 (2004)Google Scholar
- 17.Gloe, T., Böhme, R.: The dresden image database for benchmarking digital image forensics. In: Proceedings of the 2010 ACM Symposium on Applied Computing, pp. 1584–1590 (2010)Google Scholar