Copy–Move Image Forgery Detection Using Gray-Tones with Texture Description

Abstract

Copy–move forgery is a well-known image forgery technique. In this image manipulation method, a certain area of the image is replicated and affixed over the same image on different locations. Most of the times replicated segments suffer from multiple post-processing and geometrical attacks to hide sign of tampering. We have used block-based method for forgery detection. In block-based proficiencies, image is parted into partially overlapping blocks. Features are extracted corresponding to blocks. In the proposed scheme, we have computed Gray-Level Co-occurrence Matrix (GLCM) for blocks. Singular Value Decomposition (SVD) is applied over GLCM to find singular values. We have calculated Local Binary Pattern (LBP) for all blocks. The singular values and LBP features combinedly construct feature vector corresponding to blocks. These feature vectors are sorted lexicographically. Further, similar blocks discovered to identify replicated section of image. To ensure endurance of the proposed methods, Detection Accuracy (DA), False Positive Rate (FPR), and F-Measure are calculated and compared with existing methods. Experimental results establish the validity of proposed scheme for precise detection, even when meddled region of image sustain distortion due to brightness change, blurring, color reduction, and contrast adjustment.