Identification of Natural Images and Computer Generated Graphics Based on Multiple LBPs in Multicolor Spaces
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).
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