Enhancing the Arb-Syllable technique through mapping mechanism in text-based steganography

Many researchers have introduced diverse text-based steganography approaches to ensure that secret data remains undetectable. Random and statistical methods are one of the prominent methods used in text-based steganography approaches, involving hiding text according to classified characters in cover text. The existing techniques in this method are Vertical Straight Line (VERT), Changing in Alphabet Letter Patterns (CALP), and Quadruple Categorization (QUAD) techniques. However, random and statistical methods face issues in the embedding process, particularly in terms of hiding capacity, security, robustness, and imperceptibility. Therefore, this study proposes a mapping mechanism that improves the existing techniques in random and statistical method, relying on the transliteration of Arabic characters into the English language. The methodology involved developing a technique that categorizes English characters based on their Arabic transliteration, specifically whether the corresponding Arabic characters contain pointed or un-pointed letters. Experimental results demonstrated significant improvements across all performance metrics (hiding capacity, security, robustness, and imperceptibility). The proposed mapping mechanism technique achieved 100% of security ratio and imperceptibility (Jaro-Winkler distance of 1) rate, compared to maximums of 99.99% and 0.9982 for the VERT, CALP, and QUAD techniques, respectively. Additionally, hiding capacity performance is increased by 30% compared to the VERT, CALP, and QUAD techniques. Meanwhile, the proposed mapping mechanism technique demonstrated higher robustness against structural attacks, preserving the integrity of hidden messages when subjected to formatting changes. This study contributes a better approach to text-based steganography approach that balances high hiding capacity with maximum security and imperceptibility, addressing a fundamental trade-off in steganographic systems.