Open Access

ARTICLE

An Improved Image Steganography Security and Capacity Using Ant Colony Algorithm Optimization

by Zinah Khalid Jasim Jasim^*, Sefer Kurnaz^*

Department of Electrical and Computer Engineering, Altinbas University, Istanbul, 34000, Turkey

* Corresponding Authors: Zinah Khalid Jasim Jasim. Email: ; Sefer Kurnaz. Email:

(This article belongs to the Special Issue: Data and Image Processing in Intelligent Information Systems)

Computers, Materials & Continua 2024, 80(3), 4643-4662. https://doi.org/10.32604/cmc.2024.055195

Received 20 June 2024; Accepted 14 August 2024; Issue published 12 September 2024

Abstract

This advanced paper presents a new approach to improving image steganography using the Ant Colony Optimization (ACO) algorithm. Image steganography, a technique of embedding hidden information in digital photographs, should ideally achieve the dual purposes of maximum data hiding and maintenance of the integrity of the cover media so that it is least suspect. The contemporary methods of steganography are at best a compromise between these two. In this paper, we present our approach, entitled Ant Colony Optimization (ACO)-Least Significant Bit (LSB), which attempts to optimize the capacity in steganographic embedding. The approach makes use of a grayscale cover image to hide the confidential data with an additional bit pair per byte, both for integrity verification and the file checksum of the secret data. This approach encodes confidential information into four pairs of bits and embeds it within uncompressed grayscale images. The ACO algorithm uses adaptive exploration to select some pixels, maximizing the capacity of data embedding while minimizing the degradation of visual quality. Pheromone evaporation is introduced through iterations to avoid stagnation in solution refinement. The levels of pheromone are modified to reinforce successful pixel choices. Experimental results obtained through the ACO-LSB method reveal that it clearly improves image steganography capabilities by providing an increase of up to 30% in the embedding capacity compared with traditional approaches; the average Peak Signal to Noise Ratio (PSNR) is 40.5 dB with a Structural Index Similarity (SSIM) of 0.98. The approach also demonstrates very high resistance to detection, cutting down the rate by 20%. Implemented in MATLAB R2023a, the model was tested against one thousand publicly available grayscale images, thus providing robust evidence of its effectiveness.

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Keywords

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