Normalized blind STDM watermarking scheme for images and PDF documents robust against fixed gain attack

  • Makram W. HatoumEmail author
  • Rony Darazi
  • Jean-François Couchot


Spread Transform Dither Modulation (STDM), a special case of Quantization Index Modulation (QIM), has been widely used in digital watermarking. STDM has good performance in robustness against re-quantization and random noise attacks, but it is largely vulnerable to the Fixed Gain Attack (FGA). In addition to digital images and videos watermarking applications, copyright protection for digital text such as Portable Document Format (PDF) has received particular attention and interest. In this paper, we modify the STDM watermarking scheme by making the quantization step dependent on the original samples during the embedding process and on the watermarked samples during the decoding process to resist the FGA attack and enhance the robustness against the Additive White Gaussian Noise (AWGN) attack and JPEG compression attack in both the spatial domain and frequency domain regardless of the source of elements used as cover work. Experimentations have been conducted distinctly on digital images and text PDF documents. The tested images were watermarked with a uniform fidelity, where SSIM is fixed to 0.982 and 0.953. Our approach achieves significant robustness against the FGA attack with an improvement of 98% in terms of Bit Error Rates (BER) compared to traditional STDM. As for the AWGN attack, an improvement of 21% is shown. The proposed method also presents robustness against a variety of filtering and geometric attacks, while preserving a high level of transparency.


Digital watermarking Data hiding Portable Document Format Robustness 



This work is partially funded with support from the National Council for Scientific Research in Lebanon CNRS-L, the Hubert Curien CEDRE programme, the Agence Universitaire de la Francophonie AUF-PCSI programme, and the Labex ACTION program (contract ANR-11-LABX-01-01).


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Authors and Affiliations

  1. 1.FEMTO-ST InstituteUniversity of Bourgogne Franche-Comté, UMR 6174 CNRSBesançonFrance
  2. 2.TICKET LaboratoryAntonine UniversityHadat-BaabdaLebanon

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