A Novel Approach to Image Steganography: A Fusion of Chaotic and Genetic Algorithms for Unbreakable Data Hiding

Abstract

Abstract Image steganography comprises concealing sensitive data inside digital images to ensure secure communication. In this work, a ground-breaking method for image steganography is introduced, which fuses chaotic and genetic algorithms, aiming to create a strong and impenetrable data concealment technique. The study applies Integer Wavelet Transform (IWT) to the image and services logistic map chaotic system to produce pseudo-random sequences. These sequences take a significant in identifying the most secure positions within the image to embed confidential message bits. This embedding process is highly secure and resilient against statistical attacks. Moreover, a genetic algorithm optimizes this process, bolstering the security of the steganography approach. Using a population-based search technique, the genetic algorithm efficiently explores the search space, identifying the optimal chromosome representation for data embedding. This significantly enhances the embedding capacity while minimizing distortion to the original image. Through extensive experimental evaluations and performance analyses, the study showcases the effectiveness and resilience of this proposed approach. The findings highlight that combining chaotic and genetic algorithms in image steganography markedly improves security, capacity, and resistance to attacks when compared to traditional methods. This steganographic scheme achieved a PSNR of 55.3392 dB, a MSE of 0.3620, a SSIM of 0.9744, and a VIF of 0.9269. Additionally, the work demonstrated a hidden information capacity of 3.67 bits per pixel (BPP).