Design of an Efficient & Secure Steganographic Model using Modified LSB & Encryption Process

Abstract

This paper introduces a novel steganographic model for robust multimodal data security, seamlessly integrating a modified Least Significant Bit (LSB) technique with encryption, making it applicable to diverse data types such as images, audio, video, and text. Overcoming challenges posed by existing complex models and communication delays, our approach employs a modified LSB technique to encode similar sized data samples, followed by dynamic bioinspired elliptic curve cryptography (BECC) utilizing a Mayfly Optimization (MO) Model. This adaptive strategy optimizes curve types and prime key sets, significantly enhancing data security while minimizing delays and complexities across diverse data sizes. The proposed model achieves an 8.3% reduction in encryption and steganographic process delays, while simultaneously maintaining superior Peak Signal to Noise Ratio (PSNR) and lower Mean Squared Error (MSE) levels compared to existing methods when applied to the same data samples. This highlights its effectiveness in securing dynamic datasets without compromising efficiency.