Digital image forgery detection using compact multi-texture representation

Abstract

Detecting forged digital image has been an active research area in recent times. Tampering introduces artifacts within images that differentiate tampered images from authentic images. Forgery detection techniques try to identify these artifacts by analyzing differences in the texture properties of the image. In this paper, we propose a multi-texture description based method to detect tampering. Different texture descriptors considered are Local Binary Pattern, Local Phase Quantization, Binary Statistical Image Features and Binary Gabor Pattern. The method captures subtle texture variations at different scales and orientation using Steerable Pyramid Transform (SPT) decomposition of image. The different texture descriptors extracted from each subband image after SPT decomposition is combined to form the multi-texture representation. Then, ReliefF feature selection method is applied on this high dimensional multi-texture representation to generate a compact representation. This compact multi-texture representation is classified using Random Forest classifier. We have evaluated the performance of individual texture descriptors and multiple textures in detecting image forgery. Experimental results show that the compact multi-texture description has improved detection accuracy.