Stego Quality Enhancement by Message Size Reduction and Fibonacci Bit-Plane Mapping

TL;DR

This paper introduces a two-step steganography method that reduces secret image size and uses Fibonacci bit-plane mapping to improve stego image quality and robustness against detection, outperforming traditional LSB techniques.

Contribution

The paper presents a novel secret image size reduction algorithm and a Fibonacci-based embedding mechanism that enhances stego quality and security over existing methods.

Findings

Improved stego image quality compared to existing methods

Enhanced robustness against steganalysis tools like RS and WS

Increased embedding capacity through SISR ratio improvements

Abstract

An efficient 2-step steganography technique is proposed to enhance stego image quality and secret message un-detectability. The first step is a preprocessing algorithm that reduces the size of secret images without losing information. This results in improved stego image quality compared to other existing image steganography methods. The proposed secret image size reduction (SISR) algorithm is an efficient spatial domain technique. The second step is an embedding mechanism that relies on Fibonacci representation of pixel intensities to minimize the effect of embedding on the stego image quality. The improvement is attained by using bit-plane(s) mapping instead of bit-plane(s) replacement for embedding. The proposed embedding mechanism outperforms the binary based LSB randomly embedding in two ways: reduced effect on stego quality and increased robustness against statistical steganalysers. Experimental results demonstrate the benefits of the proposed scheme in terms of: 1) SISR ratio (indirectly results in increased capacity); 2) quality of the stego; and 3) robustness against steganalysers such as RS, and WS. Furthermore, experimental results show that the proposed SISR algorithm can be extended to be applicable on DICOM standard medical images. Future security standardization research is proposed that would focus on evaluating the security, performance, and effectiveness of steganography algorithms.