Copy-Move Image Forgery Detection Using Local Binary Pattern and Neighborhood Clustering

This paper introduces a copy-move image forgery detection method based on local binary patterns (LBP) and neighborhood clustering. In the proposed method, an image is first decomposed into three color components. LBP histograms are then calculated from overlapping blocks from each component. The histogram distance between the blocks is calculated and the block-pairs having the minimal distance are retained. If the retained block-pairs are present in all the three color components, they are selected as primary candidates. 8-connected neighborhood clustering is then applied to refine the candidates. The proposed method shows significant improvement in reducing the false positive rates over some recent related methods.

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