4th International ICST Conference on Security and Privacy in Communication Networks

Research Article

Detection of word shift steganography in PDF document

  • @INPROCEEDINGS{10.1145/1460877.1460897,
        author={Li Lingjun and Huang  Liusheng and Yang Wei and Zhao  Xinxin and Yu  Zhenshan and Chen Zhili},
        title={Detection of word shift steganography in PDF document},
        proceedings={4th International ICST Conference on Security and Privacy in Communication Networks},
        publisher={ACM},
        proceedings_a={SECURECOMM},
        year={2008},
        month={9},
        keywords={Text steganography steganalysis information hiding word shift PDF},
        doi={10.1145/1460877.1460897}
    }
    
  • Li Lingjun
    Huang Liusheng
    Yang Wei
    Zhao Xinxin
    Yu Zhenshan
    Chen Zhili
    Year: 2008
    Detection of word shift steganography in PDF document
    SECURECOMM
    ACM
    DOI: 10.1145/1460877.1460897
Li Lingjun1,*, Huang Liusheng1, Yang Wei1, Zhao Xinxin1, Yu Zhenshan1, Chen Zhili1
  • 1: NHPCC, USTC, Hefei, P.R. China and Suzhou Institute for Advanced Study, USTC
*Contact email: ljli@mail.ustc.edu.cn

Abstract

Word shift is a fundamental format based text steganography. It embeds secret information in text by shifting words slightly. Compared with study on steganography, research on its steganalysis is still in its infancy. In this paper, we present a blind steganalysis method to detect word shift in PDF document. Our method is to find features sensitive to word shift and use classifier to learn and remember feature differences between natural document and stego one. In order to design sensitive features, we propose two concepts "neighbor difference" and "environment equal", which reveal the spaces' statistical property. Then, we divided the PDF document into two types to make our method work efficiently. At last, we design a series of experiments to demonstrate performance of our method. The detection accuracy of our method can be up to 93.3%. Our initial results shows that our proposed concepts are very useful in text steganalysis and offer help for other related works.