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LinL:Lost in n-best List

  • Peng Meng
  • Yun-Qing Shi
  • Liusheng Huang
  • Zhili Chen
  • Wei Yang
  • Abdelrahman Desoky
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6958)

Abstract

Translation-based steganography (TBS) is a new kind of text steganographic scheme. However, contemporary TBS methods are vulnerable to statistical attacks. Differently, this paper presents a novel TBS, namely Lost in n-best List, abbreviated as LinL, that is resilient against the current statistical attacks. LinL employs only one Statistical Machine Translator (SMT) in the encoding process which selects one of the n-best list of each cover text sentence in order to camouflage messages in stegotext. The presented theoretical analysis demonstrates that there is a classification accuracy upper bound between normal translated text and the stegotext. When the text size is 1000 sentences, the theoretical maximum classification accuracy is about 60%. The experiment results also show current steganalysis methods cannot detect LinL.

Keywords

LinL natural language steganography translation-based steganography (TBS) text steganography linguistic steganography 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Peng Meng
    • 1
    • 2
    • 3
  • Yun-Qing Shi
    • 2
  • Liusheng Huang
    • 1
    • 3
  • Zhili Chen
    • 1
    • 3
  • Wei Yang
    • 1
    • 3
  • Abdelrahman Desoky
    • 4
  1. 1.NHPCC, Depart. of CS. & Tech.USTCHefeiChina
  2. 2.New Jersey Institute of TechnologyNewark, New JerseyUSA
  3. 3.Suzhou Institute for Advanced StudyUSTCSuzhouChina
  4. 4.CSEEUniversity of MarylandUSA

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