Secret Data Transmission Using Advanced Morphological Component Analysis and Steganography

Abstract

AbstractThis paper mentions how morphological component analysis, deep learning, and steganography could be used to safeguard the dissemination, recognition, and affirmation of text-based pictures over one such Internet of Things–based link. Morphological component analysis has been utilized to extract features from text-based images. Every one of those attributes may indeed, in fact, also have different morphological components. A morphological component technique decreases not only redundant but also, moreover, statistically independent features, and the dimensions in bits of data of a text-based picture to an unexpected tier of pictorial performance. Instead of relying on a singular temporal feature descriptor, the ability to obtain detailed text-based picture texture (Guo et al., In Proceedings of the Ninth IEEE International Conference on Computer Vision (ICCV). France, Nice, 2003) characteristics proves to be an essential characteristic of the indicated methodology.For the purpose of constructing morphological components, four different textured aspects (which include horizontal and vertical) were postulated all across the whole paper. Without first being transmitted through the use of Internet of Things–based networks, the morphological portions of a concealed text-based picture may be further scattered and usually embedded into the least significant bit (LSB) of a cover pixel utilizing spatial obfuscation techniques. To fetch a message from stored images, a concealed key of such an embedding process can converse with such a text retrieval method at that recipient’s edge. Finally, a combined convolution neural network approach would be used to recognize an engrained text-based message. In addition, an optimization technique would be employed to improve the hybrid convolutional neural network (HCNN) effectiveness.The findings of the analyses reveal that perhaps the visual and statistical estimations of such a cover image of the morphological component of a textual image ever seen as insertion were indeed similar to a value system upon implantation, not only removing the possibility of a highly confidential utterance being found but also empowering encrypted systems. An experiment demonstrated that the recommended technique, which integrates morphological component analysis with Daubechies filters and steganography, outperforms Haar filtering methods and techniques in terms of peak signal-to-noise ratio, structural similarity index, and accuracy.