Abstract
Due to the advancements in digital image processing and multimedia devices, the digital image can be easily tampered and presented as evidence in judicial courts, print media, social media, and for insurance claims. The most commonly used image tampering technique is the copy-move forgery (CMF) technique, where the region from the original image is copied and pasted in some other part of the same image to manipulate the original image content. The CMFD techniques may not provide robust performance after various post-processing attacks and multiple forged regions within the images. This article introduces a robust CMF detection technique to mitigate the aforementioned problems. The proposed CMF detection technique utilizes a fusion of speeded up robust features (SURF) and binary robust invariant scalable keypoints (BRISK) descriptors for CMF detection. The SURF features are robust against different post-processing attacks such as rotation, blurring, and additive noise. However, the BRISK features are considered as robust in the detection of the scale-invariant forged regions as well as poorly localized keypoints of the objects within the forged image. The fused features are matched using hamming distance and second nearest neighbor. The matched features grouped into clusters by applying density-based spatial clustering of applications with noise clustering algorithm. The random sample consensus technique is applied to the clusters to remove the remaining false matches. After some post-processing, the forged regions are detected and localized. The performance of the proposed CMFD technique is assessed using three standard datasets (i.e., CoMoFoD, MICC-F220, and MICC-F2000). The proposed technique surpasses the state-of-the-art techniques used for CMF detection in terms of true and false detection rates.