Improving the Steganographic Robustness and Capacity of Text Containers Based on Preliminary Huffman Entropy Coding

This study proposes and validates a methodology aimed at optimizing the imperceptibility and capacity characteristics of steganographic systems operating within text environments. The underlying principle involves the sequential integration of the Huffman prefix compression algorithm and the injection of hidden data via a system of Unicode zero-width control characters. Based on the computational experiment results, a 52.2% reduction in the volume of introduced artifacts was recorded. This factor proportionally minimizes the probability of detecting the hidden transmission via statistical stegananalysis (including the χ² criterion test). The high entropy level of the generated bitstream ensures data masking as natural noise. The paper provides a detailed description of the software architecture of the complex developed in Python, alongside structured evaluation results of its efficiency.