Natural Language Watermarking

  • Mohammad Ali NematollahiEmail author
  • Chalee Vorakulpipat
  • Hamurabi Gamboa Rosales
Part of the Springer Topics in Signal Processing book series (STSP, volume 11)


Nowadays, a mass traffic of Internet is occupied by text data transactions. Because text data is widely distributed, searched, and reused in various applications, it is essential to control the copyright over text as well as other forms of data including video, image, and audio. Semantic and syntactic structures of text are good candidates for embedding watermarks.


Natural Language Word Sense Disambiguation Watermark Technique Sentence Level Grammar Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Manning, C.D., and H. Schütze. 1999. Foundations of statistical natural language processing, vol. 999. MIT Press.Google Scholar
  2. 2.
    Topkara, M.K. 2007. New designs for improving the efficiency and resilience of natural language watermarking. ProQuest.Google Scholar
  3. 3.
    Linguistic Data Consortium. Google Scholar
  4. 4.
    Miller, G., and C. Fellbaum. 1998. Wordnet: An electronic lexical database. MIT Press Cambridge.Google Scholar
  5. 5.
    Topkara, M., U. Topkara, and M.J. Atallah. 2007. Information hiding through errors: a confusing approach. In Proceedings of the SPIE international conference on security, steganography, and watermarking of multimedia contents.Google Scholar
  6. 6.
    Meral, H.M., et al. 2007. Syntactic tools for text watermarking. In International society for optics and photonics electronic imaging 2007.Google Scholar
  7. 7.
    Atallah, M.J., et al. 2002. Natural language watermarking and tamperproofing. In Information hiding. Springer.Google Scholar
  8. 8.
    Bergmair, R., and S. Katzenbeisser. 2004. Towards human interactive proofs in the text-domain. In Information security, 257–267. Springer.Google Scholar
  9. 9.
    Ide, N., and J. Vronis. 1998. Word sense disambiguation: the current state of the art. Computational Linguistics, 24(1).Google Scholar
  10. 10.
    Murphy, B. 2001. Syntactic information hiding in plain text. Trinity College.Google Scholar
  11. 11.
    Singh, R., and S. Gulwani. 2012. Learning semantic string transformations from examples. Proceedings of the VLDB Endowment 5(8): 740–751.CrossRefGoogle Scholar
  12. 12.
    Ng, V., and C. Cardie. 2002. Improving machine learning approaches to coreference resolution. In Proceedings of the 40th annual meeting on association for computational linguistics. Association for Computational Linguistics.Google Scholar
  13. 13.
    Jurafsky, D. 2000. Speech & language processing. Pearson Education India.Google Scholar
  14. 14.
    Xia, F., and M. Palmer. 2001. Converting dependency structures to phrase structures. In Proceedings of the first international conference on Human language technology research. Association for Computational Linguistics.Google Scholar
  15. 15.
    Manning, D.K.C.D. 2003. Natural language parsing. In Advances in neural information processing systems 15: proceedings of the 2002 conference. MIT Press.Google Scholar
  16. 16.
    Yuan, D., et al. 2016. Word sense disambiguation with neural language models. arXiv preprint arXiv:1603.07012.
  17. 17.
    Stolcke, A. 2002. SRILM-an extensible language modeling toolkit. In INTERSPEECH.Google Scholar
  18. 18.
    Taskiran, C.M., et al. 2006. Attacks on lexical natural language steganography systems. In Electronic Imaging 2006. International Society for Optics and Photonics.Google Scholar
  19. 19.
    Reiter, E., R. Dale, and Z. Feng. 2000. Building natural language generation systems. Vol. 33. MIT Press.Google Scholar
  20. 20.
    Bourbeau, L., et al. 1990. Bilingual generation of weather forecasts in an operations environment. In Proceedings of the 13th conference on Computational linguistics, vol 3. Association for Computational Linguistics.Google Scholar
  21. 21.
    Reiter, E. 2010. Natural language generation. The handbook of computational linguistics and natural language processing, 574–598.Google Scholar
  22. 22.
    Barzilay, R., and L. Lee. 2003. Learning to paraphrase: An unsupervised approach using multiple-sequence alignment. In Proceedings of the 2003 conference of the North American chapter of the association for computational linguistics on human language technology, Vol. 1. Association for Computational Linguistics.Google Scholar
  23. 23.
    Rivest, R.L., A. Shamir, and L. Adleman. 1978. A method for obtaining digital signatures and public-key cryptosystems. Communications of the ACM 21(2): 120–126.MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Schleimer, S., D.S. Wilkerson, and A. Aiken. 2003. Winnowing: local algorithms for document fingerprinting. In Proceedings of the 2003 ACM SIGMOD international conference on Management of data. ACM.Google Scholar
  25. 25.
    Soricut, R., and D. Marcu. Stochastic language generation using WIDL-expressions and its application in machine translation and summarization. In Proceedings of the 21st international conference on computational linguistics and the 44th annual meeting of the Association for Computational Linguistics. Association for Computational Linguistics.Google Scholar
  26. 26.
    Atallah, M.J., et al. 2001. Natural language watermarking: Design, analysis, and a proof-of-concept implementation. In Information Hiding. Springer.Google Scholar
  27. 27.
    Murphy, B., and C. Vogel. 2007. The syntax of concealment: Reliable methods for plain text information hiding. In Electronic Imaging 2007. International Society for Optics and Photonics.Google Scholar
  28. 28.
    Stutsman, R., et al. 2006. Lost in just the translation. In Proceedings of the 2006 ACM symposium on applied computing. ACM.Google Scholar
  29. 29.
    Topkara, M., U. Topkara, and M.J. Atallah. 2006. Words are not enough: sentence level natural language watermarking. In Proceedings of the 4th ACM international workshop on Contents protection and security. ACM.Google Scholar
  30. 30.
    Winstein, K. 1998. Lexical steganography through adaptive modulation of the word choice hash. Unpublished.
  31. 31.
    Wayner, P. 1992. Mimic functions. Cryptologia 16(3): 193–214.MathSciNetCrossRefzbMATHGoogle Scholar
  32. 32.
    Bergmair, R. 2004. Towards linguistic steganography: A systematic investigation of approaches, systems, and issues. Final year thesis, B. Sc.(Hons.) in Computer Studies, The University of Derby.Google Scholar
  33. 33.
    Chapman, M., and G. Davida, 2002. Plausible deniability using automated linguistic stegonagraphy. In Infrastructure Security, 276–287. Springer.Google Scholar
  34. 34.
    Chapman, M., and G. Davida. 1997. Hiding the hidden: A software system for concealing ciphertext as innocuous text. In Information and Communications Security, 335–345.Google Scholar
  35. 35.
    Bergmair, R. 2007. A bibliography of linguistic steganography. In Proceedings of the SPIE international conference on security, steganography, and watermarking of multimedia contents. Citeseer.Google Scholar
  36. 36.
    Bender, W., et al. 1996. Techniques for data hiding. IBM Systems Journal, 35(3.4), 313–336.Google Scholar
  37. 37.
    Atallah, M.J., et al. 2001. Natural language processing for information assurance and security: an overview and implementations. In Proceedings of the 2000 workshop on new security paradigms. ACM.Google Scholar
  38. 38.
    Nirenburg, S., and V. Raskin, 2004. Ontological semantics.MIT Press.Google Scholar
  39. 39.
    Ellegård, A., English for the computer: The SUSANNE corpus and analytic scheme. Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2017

Authors and Affiliations

  • Mohammad Ali Nematollahi
    • 1
    Email author
  • Chalee Vorakulpipat
    • 1
  • Hamurabi Gamboa Rosales
    • 2
  1. 1.National Electronics and Computer Technology Center (NECTEC)PathumthaniThailand
  2. 2.Universidad Autónoma de ZacatecasZacatecasMexico

Personalised recommendations