Single and Multiple Copy–Move Forgery Detection and Localization in Digital Images Based on the Sparsely Encoded Distinctive Features and DBSCAN Clustering

摘要

Due to the advancements in digital image processing and multimedia devices, the digital image can be easily tamperedand presented as evidence in judicial courts, print media, social media, and for insurance claims. The most commonly usedimage tampering technique is the copy-move forgery (CMF) technique, where the region from the original image is copiedand pasted in some other part of the same image to manipulate the original image content. The CMFD techniques may notprovide robust performance after various post-processing attacks and multiple forged regions within the images. This articleintroduces a robust CMF detection technique to mitigate the aforementioned problems. The proposed CMF detectiontechnique 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-invariantforged regions as well as poorly localized keypoints of the objects within the forged image. The fused features are matchedusing hamming distance and second nearest neighbor. The matched features grouped into clusters by applying density-basedspatial clustering of applications with noise clustering algorithm. The random sample consensus technique is applied to theclusters 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 oftrue and false detection rates.