Aggregated Bidirectional Local Binary Pattern for Robust Perceptual Image Hashing
S. Qasim Abbas, S. Jannat Shirazi, Yi‐Ping Phoebe Chen · 2022
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.