Abstract
The wide use of powerful image processing software has made it easy to tamper images for malicious purposes. Image splicing, which has constituted a menace to integrity and authenticity of images, is a very common and simple trick in image tampering. Therefore, image splicing detection is of great importance in digital forensics. In this chapter, an effective framework for revealing image splicing forgery is proposed. The local binary pattern (LBP) operator is used to model magnitude components of 2-D arrays obtained by applying multi-size block discrete cosine transform (MBDCT) to the test images, all of bins of histograms computed from LBP codes are served as discriminative features for image splicing detection. To avoid the high computational complexity and possible overfitting for support vector machine (SVM) classifier, principal component analysis (PCA) is utilized to reduce the dimensionality of the proposed features. Our experiment results demonstrate the efficiency of the proposed method over the Columbia image splicing detection evaluation dataset.
Published Version
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