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Identification of Natural Images and Computer‐Generated Graphics Based on Statistical and Textural Features
Author(s) -
Peng Fei,
Li Jiaoting,
Long Min
Publication year - 2015
Publication title -
journal of forensic sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.715
H-Index - 96
eISSN - 1556-4029
pISSN - 0022-1198
DOI - 10.1111/1556-4029.12680
Subject(s) - computer science , identification (biology) , artificial intelligence , computer graphics , graphics , pattern recognition (psychology) , feature (linguistics) , computer vision , data mining , computer graphics (images) , linguistics , philosophy , botany , biology
To discriminate the acquisition pipelines of digital images, a novel scheme for the identification of natural images and computer‐generated graphics is proposed based on statistical and textural features. First, the differences between them are investigated from the view of statistics and texture, and 31 dimensions of feature are acquired for identification. Then, LIBSVM is used for the classification. Finally, the experimental results are presented. The results show that it can achieve an identification accuracy of 97.89% for computer‐generated graphics, and an identification accuracy of 97.75% for natural images. The analyses also demonstrate the proposed method has excellent performance, compared with some existing methods based only on statistical features or other features. The method has a great potential to be implemented for the identification of natural images and computer‐generated graphics.