Print-scan invariant text image watermarking for hardcopy document authentication

  • Lina Tan
  • Kai Hu
  • Xinmin Zhou
  • Rongyuan Chen
  • Weijin Jiang
Article
  • 47 Downloads

Abstract

In this paper, a novel contour feature-based text image watermarking scheme against print and scan processes is proposed. We employ a mathematical multiplicative transformation model to approximate the geometric invariant feature that can survive a variety of attacks during the print-scan process and thus serve as reference points for both watermark embedding and extraction. Based on the print-scan invariant, the boundary points of each character are flipped using Fourier descriptors with visual perception characteristics, so that the watermarks are embedded into the visually nonsignificant points. In the calculation process of the print-scan invariant, a certain text line serves as the benchmark line without affording additional characters for watermark adjustment. Thus, the hiding capacity is greatly improved. For the data detection, noise reduction and deskewing mechanisms are performed previously to compensate for the distortions caused by hardcopy. The watermark is then extracted by parity check of the invariant feature of connected components for soft authentication. The experimental results show that the proposed approach is not limited to a particular language, and has better robustness, watermark transparency as well as hiding capacity compared with some existing methods.

Keywords

Text image watermarking Print-photocopy-scan Print-scan invariant Fourier descriptors 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grants No. 61472136, 61772196,61471170), the Scientific Research Project of Hunan Provincial Education Department for the Excellent Youth Scholars (Grant No. 16B142), the Key Project of Scientific Research Fund of Hunan Provincial Education Department (Grants No. 17A113, 16A114), and the Hunan Provincial Natural Science Foundation of China (Grant No. 2016JJ2070). The authors would like to thank the financial support provided by the Key Laboratory of Hunan Province for New Retail Virtual Reality Technology (2017TP1026). The authors would also like to thank the reviewers for their insightful comments, which have greatly helped to improve the quality of this paper.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Lina Tan
    • 1
    • 2
  • Kai Hu
    • 3
  • Xinmin Zhou
    • 1
    • 2
  • Rongyuan Chen
    • 1
    • 2
  • Weijin Jiang
    • 1
    • 2
  1. 1.Key Laboratory of Hunan Province for New Retail Virtual Reality TechnologyHunan University of CommerceChangshaChina
  2. 2.Mobile E-business Collaborative Innovation Center of Hunan ProvinceHunan University of CommerceChangshaChina
  3. 3.Key Laboratory of Intelligent Computing and Information Processing of Ministry of EducationXiangtan UniversityXiangtanChina

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