LinL:Lost in n-best list

Document Type

Conference Proceeding

Publication Date

9-26-2011

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. © 2011 Springer-Verlag.

Identifier

80052987720 (Scopus)

ISBN

[9783642241772]

Publication Title

Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics

External Full Text Location

https://doi.org/10.1007/978-3-642-24178-9_23

e-ISSN

16113349

ISSN

03029743

First Page

329

Last Page

341

Volume

6958 LNCS

Grant

60903217

Fund Ref

National Natural Science Foundation of China

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