A convolutional neural network model based reversible data hiding scheme in encrypted images with block-wise Arnold transform

Abstract

The research in the domain of reversible data hiding (RDH) is recently explored in all aspects due to its applications in cloud computing, forensics, and medical image communication. In this manuscript, we introduce a RDH scheme in encrypted images which can provide a high embedding rate without compromising the bit error rate during the message extraction and image recovery. The proposed scheme follows a block-wise data hiding process. If the processed block size is A×A pixels, and if A=2n+1, then the data hider can hide any number from the set {0, 1, …, (A+2n−1)} in that block. We have introduced an Arnold transform-based data hiding process in which each block will be undergoing a series of scrambling processes based on the bit sequence that we need to embed in the selected block. The message extraction and image restoration are carried out at the receiver side using a trained convolutional neural network (CNN) model.