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Digital images can be manipulated using the latest tools and techniques without leaving any visible traces. Image tampering detection is required to authenticate image validation. It is concluded from previous research that image tampering modifies the texture micropattern in a digital image. Therefore, texture descriptors can be applied to highlight these changes. A texture descriptor–based technique is proposed for detecting both copy-move and splicing forgery. In the proposed method, an RGB image is converted into a YCbCr image and Cb and Cr image components are extracted, as these components are more sensitive to tampering artifacts. Further, a standard deviation (STD) filter and higher-order texture descriptors are applied on Cb and Cr components. The STD filter is used to highlight important details of objects in the image. A support vector machine classifier is used to classify forged and tampered images. Support vector machine (SVM) classifier gives good results on both large- and small-image data sets. The performance is appraised in three online-available, widely used data sets: CASIA v1.0, CASIA v2.0, and Columbia. The proposed method outperforms most of the state-of-the-art methods.

Digital Image Tampering Detection Using Multilevel Local Binary Pattern Texture Descriptor