An Intelligent Multi-Objective Optimization Approach for High-Capacity Secure Image Steganography

A. V. Gahan; Geetha D. Devanagavi

doi:10.48084/etasr.18267

Authors

A. V. Gahan School of Electronics and Communication Engineering, REVA University, Bengaluru, India
Geetha D. Devanagavi School of Computer Science and Engineering, REVA University, Bengaluru, India

Volume: 16 | Issue: 3 | Pages: 34973-34981 | June 2026 | https://doi.org/10.48084/etasr.18267

Received: 19 February 2026 | Revised: 12 March 2026 | Accepted: 23 March 2026 | Online: 6 April 2026
Corresponding author: A. V. Gahan

Abstract

In this paper, an optimization-based and secure image steganography model, termed Multi-Objective Adaptive Firefly Optimized Secure Steganography (MOAFOSS), is proposed. The model formulates pixel selection as a multi-objective optimization problem to minimize Mean Square Error (MSE), maximize Peak Signal-to-Noise Ratio (PSNR), maximize entropy, and minimize the Standard Deviation (SD) to increase its resistance to steganalysis attacks. The Firefly Algorithm (FA) is used to find the best embedding points using a fitness criterion defined as a brightness function based on joint fitness. In addition, an adaptive Least Significant Bit (LSB) embedding mechanism is employed to conditionally adjust pixel values in order to minimize visual artifacts while maintaining accurate embedding. The additional encryption of secret data before embedding further enhances confidentiality. The experimental outcomes demonstrate that the suggested framework possesses better embedding capacity and Compression Ratio (CR) with a lower level of perceptible distortion. A comparative study with conventional optimization and compression methods demonstrates that MOAFOSS offers a balanced trade-off between image quality, security strength, and the amount of data carried, making it suitable for secure digital communication applications.

Keywords:

multi-objective optimization, image steganography, Firefly Algorithm (FA), adaptive LSB embedding, secure data hiding, entropy-based security, steganalysis resistance, Compression Ratio (CR)

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