Robust data hiding scheme through distinct keypoint selection exploiting modified Bilateral-Laplacian SIFT with encoding pipeline

Abstract

SIFT (Scale Invariant Feature Transform) has recently been used to secure multimedia materials from geometric and non-geometric attacks. SIFT is commonly used in Computer Vision to identify and describe local features in images using the Difference of Gaussian (DoG) method, which has a high processing cost and a strong affinity towards the edge, resulting in a low contrast point. By introducing modified SIFT (mSIFT), which selects the most distinguishable key point with their feature descriptors, an attempt has been made to construct a rotation and scaling resistant data hiding strategy in this study. Unlike SIFT, a Bilateral-Laplacian filter was employed to determine the extrema, which took into account both geometric and photometric distance to reduce noise and retain the edge. This adds to the benefits of the proposed scheme’s resistance against geometric attacks, even when the tampering rate is large. Furthermore, during data embedding, the suggested technique achieves excellent security by utilizing Arnold chaotic function, pseudorandom sequence generator, and a newly created unique encoding pipeline. The proposed technique is secure and resistant to various steganographic attacks while preserving good visual quality, according to experimental results and analysis, as well as comparisons with existing state-of-the-art methods.