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A robust watermarking approach for security issue of binary documents using fully convolutional networks

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Abstract

Motivated by increasing possibility of the tampering of genuine documents during a transmission over digital channels, we focus on developing a watermarking framework for determining whether a received document is genuine or falsified, which is performed by hiding a security feature or secret information within it. To begin with, the input document is transformed into a standard form to minimize geometric distortion. Fully convolutional network (FCN) is utilized to detect document’s watermarking regions. Next, we construct hiding patterns used for hiding secret information. Modifying pixel values of these patterns for carrying secret bits depends on the edge and corner features of document content and the connectivity of their neighboring pixels. Lastly, the watermarking process is conducted by either changing the center pixel of the hiding patterns or changing the ratio between the number of edge features and the number of corner features of subregions within the watermarking regions. The experiments are performed on various binary documents, and our approach gives competitive performance compared to state-of-the-art approaches.

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Acknowledgements

This work is supported by the CPER NUMERIC programme funded by the Region Nouvelle Aquit-aine, CDA, Charente Maritime French Department, La Roche-lle conurbation authority (CDA) and the European Union through the FEDER funding.

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Authors and Affiliations

  1. Can Tho University, Can Tho, Vietnam

    Vinh Loc Cu

  2. Institute of Research and Development, Duy Tan University, Danang, 550000, Vietnam

    Trac Nguyen

  3. L3i Laboratory, La Rochelle University, La Rochelle, France

    Vinh Loc Cu, Jean-Christophe Burie & Jean-Marc Ogier

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  1. Vinh Loc Cu
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  2. Trac Nguyen
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  3. Jean-Christophe Burie
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  4. Jean-Marc Ogier
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Correspondence to Trac Nguyen.

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Cu, V.L., Nguyen, T., Burie, JC. et al. A robust watermarking approach for security issue of binary documents using fully convolutional networks. IJDAR 23, 219–239 (2020). https://doi.org/10.1007/s10032-020-00355-z

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  • DOI: https://doi.org/10.1007/s10032-020-00355-z

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