TY - GEN
T1 - A statistical algorithm for linguistic steganography detection based on distribution of words
AU - Zhi-Li, Chen
AU - Liu-Sheng, Huang
AU - Zhen-Shan, Yu
AU - Ling-Jun, Li
AU - Wei, Yang
PY - 2008
Y1 - 2008
N2 - In this paper, a novel statistical algorithm for linguistic steganography detection, which takes advantage of distribution of words in the text segment detected, is presented. Linguistic steganography is the art of using written natural language to hide the very presence of secret messages. Using the text data, which is the foundational media in internet communications, as its carrier, linguistic steganography plays an important part in Information Hiding (IH) area. The previous work was mainly focused on linguistic steganography and there were few researches on linguistic steganalisys. We attempt to do something to help to fix this gap. In our experiment of detecting the three different linguistic steganography methods: NICETEXT, TEXTO and Markov-Chain-Based, the total accuracies on discovering stego-text segments and normal text segments are found to be 87.39%, 95.51%, 98.50%, 99.15% and 99.57% respectively when the segment size is 5kB, 10kB, 20kB, 30kB and 40kB. Our research shows that the linguistic steganalysis based on distribution of words is promising.
AB - In this paper, a novel statistical algorithm for linguistic steganography detection, which takes advantage of distribution of words in the text segment detected, is presented. Linguistic steganography is the art of using written natural language to hide the very presence of secret messages. Using the text data, which is the foundational media in internet communications, as its carrier, linguistic steganography plays an important part in Information Hiding (IH) area. The previous work was mainly focused on linguistic steganography and there were few researches on linguistic steganalisys. We attempt to do something to help to fix this gap. In our experiment of detecting the three different linguistic steganography methods: NICETEXT, TEXTO and Markov-Chain-Based, the total accuracies on discovering stego-text segments and normal text segments are found to be 87.39%, 95.51%, 98.50%, 99.15% and 99.57% respectively when the segment size is 5kB, 10kB, 20kB, 30kB and 40kB. Our research shows that the linguistic steganalysis based on distribution of words is promising.
UR - https://www.scopus.com/pages/publications/49049104223
U2 - 10.1109/ARES.2008.61
DO - 10.1109/ARES.2008.61
M3 - 会议稿件
AN - SCOPUS:49049104223
SN - 0769531024
SN - 9780769531021
T3 - ARES 2008 - 3rd International Conference on Availability, Security, and Reliability, Proceedings
SP - 558
EP - 563
BT - ARES 2008 - 3rd International Conference on Availability, Security, and Reliability, Proceedings
PB - IEEE Computer Society
T2 - 3rd International Conference on Availability, Security, and Reliability, ARES 2008
Y2 - 4 March 2008 through 7 March 2008
ER -