Progressive halftone watermarking using multilayer table lookup strategy

Jing Ming Guo, Guo Hung Lai, Koksheik Wong, Li Chung Chang

Research output: Contribution to journalArticleResearchpeer-review

13 Citations (Scopus)


In this paper, a halftoning-based multilayer watermarking of low computational complexity is proposed. An additional data-hiding technique is also employed to embed multiple watermarks into the watermark to be embedded to improve the security and embedding capacity. At the encoder, the efficient direct binary search method is employed to generate 256 reference tables to ensure the output is in halftone format. Subsequently, watermarks are embedded by a set of optimized compressed tables with various textural angles for table lookup. At the decoder, the least mean square metric is considered to increase the differences among those generated phenotypes of the embedding angles and reduce the required number of dimensions for each angle. Finally, the naïve Bayes classifier is employed to collect the possibilities of multilayer information for classifying the associated angles to extract the embedded watermarks. These decoded watermarks can be further overlapped for retrieving the additional hidden-layer watermarks. Experimental results show that the proposed method requires only 8.4 ms for embedding a watermark into an image of size 512 x 512, under the 32-bit Windows 7 platform running on 4GB RAM, Intel core i7 Sandy Bridge with 4GB RAM and IDE Visual Studio 2010. Finally, only 2 MB is required to store the proposed compressed reference table.

Original languageEnglish
Pages (from-to)2009-2024
Number of pages16
JournalIEEE Transactions on Image Processing
Issue number7
Publication statusPublished - 1 Jul 2015
Externally publishedYes


  • data hiding
  • Halftoning
  • least mean square
  • naïve Bayes classifier
  • watermarking

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