Adaptive clustering-based approach for forgery detection in images containing similar appearing but authentic objects

Abstract

Copy-move forgery is one of the well-known image forgery technique which exploits regions of the same image to create forged image by replicating or hiding authentic content of the original image. Original images can also contain similar looking but authentic objects. In such cases, identification of authentic and tampered images is a complicated task. To tackle this problem, we propose a method in which Stationary Wavelet Transform (SWT) and spatial-constrained edge preserving watershed segmentation are applied over input image in preprocessing step. Keypoint extraction and descriptor computation are performed using Cascaded Features from Accelerated Segment Test (Cascaded FAST) and Binary Robust Invariant Scalable Keypoint (BRISK) descriptor, respectively. Approximate nearest neighbor search is performed using Random Binary Search Tree (RBST) method. For keypoint clustering, Adaptive Density Peak Clustering (ADPC) technique is employed. Outlier removal is performed using Random Sample Consensus (RANSAC) technique. Further, forged regions are localized using correlation map generation. Experimental results display that the proposed approach can effectively distinguish between forged and original images containing similar appearing but authentic objects. It is also able to detect forged images sustaining different post-processing attacks. For COVERAGE dataset, proposed technique achieves high F-Measure = 86.901% and low False Positive Rate (FPR) = 15.241% in comparison to state-of-the-art techniques.