ENHANCING KNOWLEDGE SECURITY THROUGH TEXT STEGANOGRAPHY: A REVIEW OF TECHNIQUES AND TOOLS

Abstract

This study addresses the increasing challenges of ensuring data security and privacy in the digital age, specifically focusing on the limitations of current steganography techniques in maintaining a balance between embedding capacity and the naturalness of text. As digital threats continue to evolve, traditional steganography methods, though effective in concealing information, often compromise the coherence of the text or limit the volume of hidden data. The aim of this study is to explore and analyze emerging technologies and methods that enhance the robustness of text steganography while preserving the integrity and readability of the text. The literature review highlights various approaches, such as synonym substitution, sentence restructuring, and the use of invisible characters, as well as advanced techniques like deep learning and generative adversarial networks (GANs) to improve data embedding. It also examines tools like Steghide, OpenStego, and SilentEye, which are widely used for steganography, yet face challenges in security and usability. The main findings indicate that AI and machine learning-driven techniques significantly improve the capacity and imperceptibility of hidden data while minimizing detection through steganalysis. Additionally, integrating steganography with cryptography and blockchain technologies offers enhanced security by ensuring that hidden data remains protected even if detected. These findings have significant implications for secure communication, suggesting that further refinement of these methods could lead to more robust and adaptive steganographic systems capable of withstanding increasingly sophisticated detection techniques. This study encourages ongoing research into the development of new tools and technologies to address the growing need for secure and covert data transmission.This abstract provides a concise summary of the study, highlighting the problem, aims, literature review, findings, and implications.