A Fast and Low Distortion Image Steganography Framework Based on Nature-Inspired Optimizers

Abstract

Nowadays, secret information is susceptible to numerous hacks while being shared through traditional correspondence channels. Unlike other methods of securing data, steganography offers the most comprehensive security. Several algorithms have been developed to determine the optimal embedding capacity of every pixel to enhance steganography techniques. Concealing secret bits is not the only challenging aspect of this operation; improving the embedding capacity of secret data is also important, as is maintaining an acceptable visual quality after concealment. A new framework is proposed for concealing a secret text in the spatial domain by partitioning the host image into some non-overlapping blocks and classifying each block as edged or smooth using the Whale Optimization Algorithm (WOA). Different WOA objective functions can be utilized to determine each pixel’s embedding capacity based on its intensity. For smooth blocks, the fitness function (with low objective values) is used, and for edge blocks, the cost function (with high objective values) is used instead. Additionally, several combinations of scanning order and starting point for each block in the host image are determined to reduce embedding distortion. WOA can be used to determine the best combination of them. Minimum replacement error (MRE) and compensated grayscale changes (CGSC) modules further reduce embedded distortion and artifacts. Accordingly, the recommended technique produces a peak signal-to-noise ratio (PSNR) of 62.44 dB, compared to 43.15 dB using the benchmark algorithm Multiple Pixel-value Adjustment (MPA) with an encoding function algorithm. Due to its superior performance, the recommended framework outperforms existing frameworks.

Keywords

• Data hiding
• pixel embedding capacity
• steganography
• spatial domain
• visual distortion, WOA