Splicing image forgery detection using textural features based on the grey level co‐occurrence matrices. Issue 1 (1st January 2017)

• Record Type: Journal Article
• Authors: Shen, Xuanjing; Shi, Zenan; Chen, Haipeng
• Is Part Of: IET image processing. Volume 11:Issue 1(2017)
• Page Start: 44
• Page End: 53
• Publication Date: 2017-01-01
• Subjects: image texture -- grey systems -- matrix algebra -- support vector machines -- computational complexity; splicing image forgery detection -- textural features -- grey level co‐occurrence matrices -- passive splicing detection method -- TF‐GLCM -- discrete cosine transform arrays -- textural information -- image pixels -- statistical moments -- support vector machine -- computational complexity -- 96‐D feature vector -- CASIA; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2016.0238
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : To further improve the detection rate with relatively low dimension feature vector, a novel passive splicing detection method using textural features based on the grey level co‐occurrence matrices, namely TF‐GLCM, is proposed in this study. In the TF‐GLCM, the GLCM are calculated based on the difference block discrete cosine transform arrays to capture the textural information and the spatial relationship between image pixels sufficiently. The discriminable properties contained in the GLCM are described by six textural features, which include two new introduced ones and four independent ones. In addition, the statistical moments mean Me and standard deviation SD of textural features are used instead of themselves as elements in feature vector to reduce the dimensionality of feature vector and computational complexity. A support vector machine is employed for classification purpose. Experimental results show that the TF‐GLCM achieves the detection rates of 98% on CASIA v1.0, and 97% on CASIA v2.0 with 96‐D feature vector. The detection rates benefit from the two new textural features. Meanwhile, the TF‐GLCM is superior to some state‐of‐the‐art methods with lower dimension feature vector.