Aggregated Bidirectional Local Binary Pattern for Robust Perceptual Image Hashing

Easy access and speedy advancements to image modification tools have made it tough for researchers and experts working in the domain of image integrity verification. It has ever become a grueling job in present times. Robust Perceptual Image Hashing (RPIH) is one of the available approaches to verify image integrity. RPIH algorithms are developed for extracting a specific group of designated features from a query image to produce a consolidated representation, which can be utilized to verify image integrity. In this paper, a novel aggregated bidirectional local binary pattern RPIH algorithm is proposed, to compute the image hash, which constitutes fixed-length data. We combine Local binary Pattern (LBP) and Reverse Local Binary Pattern (RLBP) histograms to generate Aggregated Bidirectional Local Binary Pattern (AB-LBP) features for creating a hash vector. The experimental results demonstrate the usefulness of the proposed AB-LBP algorithm as a RPIH scheme and its superior computation efficiency. Additionally, the AB-LBP scheme conveys information to localize the tamper location, which is handy in rejecting the selective part of the tampered image.