Secret Image Sharing Using Grayscale Payload Decomposition and Irreversible Image Steganography

TL;DR

This paper introduces a low-complexity secret image sharing method that decomposes a grayscale payload into multiple matrices and embeds them into cover images using bitwise XOR, enhancing security with minimal computational overhead.

Contribution

The proposed scheme replaces traditional encryption with payload diversification into matrices and embedding via XOR, reducing complexity and maintaining security in steganography.

Findings

Effectively camouflages payload with minimal computation time

Achieves secure image sharing without complex encryption

Maintains low distortion in steganographic embedding

Abstract

To provide an added security level most of the existing reversible as well as irreversible image steganography schemes emphasize on encrypting the secret image (payload) before embedding it to the cover image. The complexity of encryption for a large payload where the embedding algorithm itself is complex may adversely affect the steganographic system. Schemes that can induce same level of distortion, as any standard encryption technique with lower computational complexity, can improve the performance of stego systems. In this paper we propose a secure secret image sharing scheme, which bears minimal computational complexity. The proposed scheme, as a replacement for encryption, diversifies the payload into different matrices which are embedded into carrier image (cover image) using bit X-OR operation. A payload is a grayscale image which is divided into frequency matrix, error matrix, and sign matrix. The frequency matrix is scaled down using a mapping algorithm to produce Down Scaled Frequency (DSF) matrix. The DSF matrix, error matrix, and sign matrix are then embedded in different cover images using bit X-OR operation between the bit planes of the matrices and respective cover images. Analysis of the proposed scheme shows that it effectively camouflages the payload with minimum computation time.