Image Tampering Detection Based on Local Texture Descriptor and Extreme Learning Machine

Abstract

This paper proposed a new image tampering detection method based on local texture descriptor and extreme learning machine (ELM). The image tampering includes both splicing and copy-move forgery. First, the image was decomposed into three color channels (one luminance and two Chroma), and each channel was divided into non-overlapping blocks. Local textures in the form of local binary pattern (LBP) were extracted from each block. The histograms of the patterns of all the blocks were concatenated to form a feature vector. The feature vector was then fed to an ELM for classification. The ELM is a powerful and fast classification approach. The experiments was performed using two publicly available databases. The experimental results showed that the proposed method achieved a high detection accuracy in both the databases.