Abstract
Due to the maturing of digital image processing techniques, there are many tools that can forge an image easily without leaving visible traces and lead to the problem of the authentication of digital images. Based on the assumption that forgery alters the texture micro-patterns in a digital image and texture descriptors can be used for modeling this change; we employed two stat-of-the-art local texture descriptors: multi-scale Weber's law descriptor (multi-WLD) and multi-scale local binary pattern (multi-LBP) for splicing and copy-move forgery detection. As the tamper traces are not visible to open eyes, so the chrominance components of an image encode these traces and were used for modeling tamper traces with the texture descriptors. To reduce the dimension of the feature space and get rid of redundant features, we employed locally learning based (LLB) algorithm. For identifying an image as authentic or tampered, Support vector machine (SVM) was used. This paper presents the thorough investigation for the validation of this forgery detection method. The experiments were conducted on three benchmark image data sets, namely, CASIA v1.0, CASIA v2.0, and Columbia color. The experimental results showed that the accuracy rate of multi-WLD based method was 94.19% on CASIA v1.0, 96.52% on CASIA v2.0, and 94.17% on Columbia data set. It is not only significantly better than multi-LBP based method, but also it outperforms other stat-of-the-art similar forgery detection methods.