Visual Cryptography Based Lossless Watermarking for Sensitive Images

  • Surekha BorraEmail author
  • Viswanadha Raju S.
  • Lakshmi H.R.
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9873)


Digital technology has resulted in a cost effective and easy means for storage and communication of the multimedia data, which can also be easily downloadable and reproducible. Hence, there is a growing need for techniques that identify the right owner of Intellectual Property (IP). Visual Cryptography based Lossless Watermarking (VCLW) techniques are hybrid schemes that are now being developed as a potential replacement for conventional watermarking systems as they are effective in resolving existing tradeoff among the requirements. This paper presents theoretical analysis of spatial domain VCLW techniques and performs practical performance analysis, to identify gaps. These hybrid techniques offer promising quality of target image after watermarking; there are still a number of challenges associated such as security, robustness, pixel expansion, complexity, image formats etc. Among multiple requirements, achieving fewer rates of false positives is identified as fundamental requirement in order to discourage malicious owners and to protect Intellectual Property. The analysis of comparative results shows that with the existing techniques, the false positive rate could not reach 10−6, which is the maximum limit suggested by Cox et al. for watermarking. This is due to the unique security challenge called C3 rule that involves the design of code tables, selection of features, thresholds and combination functions. It is concluded that in the applications where the images to be protected are very sensitive such as military, medical and satellite images, VCLW techniques can be a potential replacement for conventional watermarking systems, given that the algorithms fills the important gaps.


Target Image JPEG Compression High False Positive Rate Copyright Protection Combination Function 
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 International Publishing AG 2016

Authors and Affiliations

  • Surekha Borra
    • 1
    Email author
  • Viswanadha Raju S.
    • 2
  • Lakshmi H.R.
    • 1
  1. 1.Department of ECEK.S. Institute of TechnologyBangaloreIndia
  2. 2.Department of CSEJNTUHCEJ, JNT UniversityHyderabadIndia

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