Adaptive Invisible Steganography Leveraging Entropy-Based Image Encryption with the Mimic Octopus Adaptive Framework (MOAF)

In the realm of secure communication, invisible steganography plays a crucial role by allowing discreet data transmission within digital images while preserving their visual integrity. This study introduces a pioneering method aimed at enhancing the security and concealment of steganographic data. It achieves this by utilizing entropy-based image encryption in combination with a novel Mimic Octopus Adaptive Framework (MOAF). The primary objective of MOAF is to establish a higher level of image security, denoted as the third tier, by dynamically selecting suitable compression and encryption algorithms based on the entropy characteristics of the secret image. Our approach attained an impressive peak signal-to-noise ratio (PSNR) of 81.31 when comparing the secret image to the decompressed image, with a payload size of 79,747 bytes. Additionally, the structural similarity index (SSIM) achieved a perfect score of 1.0 under the same payload conditions. Comprehensive experiments and comparative analyses against established steganographic techniques demonstrate that our proposed system excels in security, imperceptibility, and resilience against various attacks. These findings underscore the potential of entropy-based image encryption, combined with the innovative Mimic Octopus Adaptive Framework (MOAF), to advance invisible steganography and enable more secure and clandestine data transmission in the digital age.