Reversible data hiding using least square predictor via the LASSO

Abstract

Reversible watermarking is a kind of digital watermarking which is able to recover the original image exactly as well as extracting hidden message. Many algorithms have aimed at lower image distortion in higher embedding capacity. In the reversible data hiding, the role of efficient predictors is crucial. Recently, adaptive predictors using least square approach have been proposed to overcome the limitation of the fixed predictors. This paper proposes a novel reversible data hiding algorithm using least square predictor via least absolute shrinkage and selection operator (LASSO). This predictor is dynamic in nature rather than fixed. Experimental results show that the proposed method outperforms the previous methods including some algorithms which are based on the least square predictors.