Unveiling Hidden Messages: A Robust Approach to Detecting Structural Text Steganography in Office Open XML Documents
F. R. · 2024
This paper presents an innovative approach to de-tecting text structural steganography based on Office Open XML (OOXML). Our technique uses paragraph vector-distributed memory and logistic regression to analyze invisible characters and font color manipulations. We built a steganalysis scheme and trained a model that accurately distinguishes whether an OOXML document is a stego file or a normal one. Our results show outstanding performance with a testing accuracy of 98 %, an F1 score of 97 %, and an average precision-recall score of 97 %. Additionally, we introduced the Structural Integrity Score (SIS) to evaluate the extent to which the original document structure is preserved after embedding hidden messages. We benchmarked our method against current state-of-the-art steganalysis techniques for text steganography, demonstrating superior performance.