Enhanced file security system with PVD encoding for multiple image steganography

Abstract

To improve security during message transmission and guarantee the safety of data sharing between various military parties, our proposed method involves developing a web application that can hide information in any picture file. The goal of this project is to provide steganography tools that can conceal data, images, and user-specified paths for saving images and extruded files. We employ the LSB method. A steganography method is suggested as a means of military data embedding in picture files. We employed a module-based facial recognition system in conjunction with the AES algorithm to provide robust security. Additionally, we employ the cover channel approach to conceal data that processes can utilize to transport data in a way that goes against the rules of the system's security. We also make advantage of copyright marking strategies. To summarize, Cover Channels transmit data in a way that goes against the system's intended architecture by employing non-standard ways. Three main types of deep learning approaches are utilized for picture steganography: classic methods, methods based on Convolutional Neural Networks, and methods based on General Adversarial Networks. This article describes the approach as well as the datasets utilized, experimental setups that were explored, and the assessment measures that were often employed. For your convenience, we have also included a table that summarizes all the facts. By outlining the state of the area, its problems, and potential future directions, this work hopes to be of use to other scholars in the field.