Robust Numeric Set Watermarking: Numbers Don’t Lie

  • Gaurav Gupta
  • Josef Pieprzyk
  • Mohan Kankanhalli
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 130)


Ever since Cox et. al published their paper, “A Secure, Robust Watermark for Multimedia” in 1996 [6], there has been tremendous progress in multimedia watermarking. The same pattern re-emerged with Agrawal and Kiernan publishing their work “Watermarking Relational Databases” in 2001 [1]. However, little attention has been given to primitive data collections with only a handful works of research known to the authors [11, 10]. This is primarily due to the absence of an attribute that differentiates marked items from unmarked item during insertion and detection process. This paper presents a distribution-independent, watermarking model that is secure against secondary-watermarking in addition to conventional attacks such as data addition, deletion and distortion. The low false positives and high capacity provide additional strength to the scheme. These claims are backed by experimental results provided in the paper.


Watermark Scheme Watermark Detection False Positive Probability Watermark Insertion Watermark Capacity 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Gaurav Gupta
    • 1
    • 2
  • Josef Pieprzyk
    • 1
  • Mohan Kankanhalli
    • 3
  1. 1.Centre for Advanced Computing, Algorithms and Cryptography Department of ComputingMacquarie UniversitySidneyAustralia
  2. 2.Centre for Policing, Intelligence and Counter TerrorismMacquarie UniversitySidneyAustralia
  3. 3.School of ComputingNational University of SingaporeSingapore

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