Optimising Text Steganography with Alpha-Based Representative Binary: Analysing Contain Letter Used and Capacity Ratio

Abstract

Text steganography has become essential for secure communication in the digital age, offering a method to hide messages within seemingly ordinary text. This paper introduces the Alpha-based Representative Binary technique, an advanced feature-based method for text steganography. It utilizes both capital and small letters to represent 2-bit binary values, embedding more information without altering the visual integrity of the text. By assigning different binary values to uppercase and lowercase letters, this technique optimizes the balance between information density and detectability. Embedding 2-bit values strikes an ideal balance, as it allows for a higher capacity of hidden information while maintaining a low profile. This study evaluates the performance of the Alpha-based Representative Binary technique against existing methods like QUAD and One-Flow-2-bit, focusing on two metrics performance: the total character count in the stego text (contain letter used) and the amount of data that can be embedded relative to the cover text size (capacity ratio). The findings underscore the technique's strengths in maintaining secure and undetectable hidden messages within text. This paper highlights the evolution and effectiveness of feature-based methods, emphasizing the need for continued innovation to address detection risks and content modification challenges. By providing a detailed analysis and potential improvements, this paper aims to contribute to the development of more robust and undetectable text steganography systems. The perceptions gained from this paper is expected as valuable for researchers and practitioners in the field of secure communication.