An Advanced Morphological Component Analysis, Steganography, and Deep Learning-Based System to Transmit Secure Textual Data

Abstract

A potential to extract detailed textual image texture features is a key characteristic of the suggested approach, instead of using a single spatial texture feature. For the generation of MCs, four textured characteristics (including horizontal and vertical) are assumed in this paper that are content, coarseness, contrast, and directionality. The morphological parts of a clandestine text-based image were further segmented and then usually inserted into the least significant bit in cover pixels utilising spatial steganography. This same reverse process for steganography and MCA is conducted on the recipient side after transmission. The results demonstrate that the proposed method based on fusion of MCA and steganography provides a higher performance measure, for instance peak signal-to-noise ratio, SSIM, than the previous method.