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This study proposes an advanced watermarking technique for Digital Elevation Model (DEM) data, combining the cryptographic resilience via Shamir’s Secret Sharing and the integrity enhancement via Generative Adversarial Network (GAN). This method embeds a watermark into binary image carriers that are converted from DEM data, and the watermark can be reconstructed if the number of loss is no more than a threshold value. The usage of GAN ensures the seamless integration of the watermark, preserving the integrity of the original DEM data. Our experimental results and analysis demonstrate the exceptional performance of the proposed method, particularly its high resistance to a range of simulated attacks and its ability to maintain the fidelity of the DEM data, which justified that our method can be applied for data provenance of typical geospatial data.

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A Robust Data Watermarking Method Based on Secret Sharing and GAN for Digital Elevation Model

  • 会议论文

  • 发表日期：

    2026年

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  • Jinge Ma,
  • Jia Duan,
  • Xi Liu,
  • Xianghan Zheng,
  • Wei Ren

摘要

This study proposes an advanced watermarking technique for Digital Elevation Model (DEM) data, combining the cryptographic resilience via Shamir’s Secret Sharing and the integrity enhancement via Generative Adversarial Network (GAN). This method embeds a watermark into binary image carriers that are converted from DEM data, and the watermark can be reconstructed if the number of loss is no more than a threshold value. The usage of GAN ensures the seamless integration of the watermark, preserving the integrity of the original DEM data. Our experimental results and analysis demonstrate the exceptional performance of the proposed method, particularly its high resistance to a range of simulated attacks and its ability to maintain the fidelity of the DEM data, which justified that our method can be applied for data provenance of typical geospatial data.