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<p>Healthcare organisations are increasingly vulnerable to cyberattacks that target electronic health records (EHRs) and associated medical images, posing a threat to both patient privacy and the continuity of healthcare services. To strengthen the protection of these critical assets, this work presents a watermarking scheme that embeds patient-specific information—comprising a diagnosis report, fingerprint, and identity code—into medical images. The approach combines Integer Wavelet Transform (IWT), Singular Value Decomposition (SVD), and Principal Component Analysis (PCA) to achieve a balance between imperceptibility, robustness, and reversibility. The design was implemented on an Xilinx Zynq XC7Z020 platform, ensuring hardware efficiency while maintaining resilience against a wide range of image-related attacks. Experimental evaluation confirms that the proposed method preserves image quality and enables reliable watermark recovery, making it suitable for secure management of healthcare data. The watermarking embedding procedure experimentation was carried out using Zynq- XC7z020-CLG484I and utilises 33% of the LUTs, 574 registers, and 82 carry 4 adders. The proposed work achieves an average Peak Signal-to-Noise Ratio (PSNR) of 83 dB, a Structural Similarity Index Metric (SSIM) greater than 0.7, and a Normalised Cross-Correlation (NCC) of 1. The resultant image was intentionally subjected to 17 different attacks. The average PSNR, SSIM, and NCC between the 8-bit original watermark and the extracted watermark after attacks were 42 dB, 0.99, and 1, respectively. Eventually, the average values for the same were calculated between the 16-bit original and extracted cover after attacks were 94 dB, 0.99 and 1, respectively. These results demonstrate that the proposed work offers better imperceptibility (PSNR), robustness (NCC), and reversibility (SSIM) even after various image-related attacks.</p>

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FPGA accelerated near-loseless robust watermarking framework for healthcare cybersecurity applications

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月6日

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  • A. Sridevi,
  • Janakiraman Siva,
  • R. Sivaraman,
  • R. Raj Vinoth,
  • S. Banu Aashiq,
  • R. Dhivya,
  • M. Hemalatha,
  • Amirtharajan Rengarajan

摘要

Healthcare organisations are increasingly vulnerable to cyberattacks that target electronic health records (EHRs) and associated medical images, posing a threat to both patient privacy and the continuity of healthcare services. To strengthen the protection of these critical assets, this work presents a watermarking scheme that embeds patient-specific information—comprising a diagnosis report, fingerprint, and identity code—into medical images. The approach combines Integer Wavelet Transform (IWT), Singular Value Decomposition (SVD), and Principal Component Analysis (PCA) to achieve a balance between imperceptibility, robustness, and reversibility. The design was implemented on an Xilinx Zynq XC7Z020 platform, ensuring hardware efficiency while maintaining resilience against a wide range of image-related attacks. Experimental evaluation confirms that the proposed method preserves image quality and enables reliable watermark recovery, making it suitable for secure management of healthcare data. The watermarking embedding procedure experimentation was carried out using Zynq- XC7z020-CLG484I and utilises 33% of the LUTs, 574 registers, and 82 carry 4 adders. The proposed work achieves an average Peak Signal-to-Noise Ratio (PSNR) of 83 dB, a Structural Similarity Index Metric (SSIM) greater than 0.7, and a Normalised Cross-Correlation (NCC) of 1. The resultant image was intentionally subjected to 17 different attacks. The average PSNR, SSIM, and NCC between the 8-bit original watermark and the extracted watermark after attacks were 42 dB, 0.99, and 1, respectively. Eventually, the average values for the same were calculated between the 16-bit original and extracted cover after attacks were 94 dB, 0.99 and 1, respectively. These results demonstrate that the proposed work offers better imperceptibility (PSNR), robustness (NCC), and reversibility (SSIM) even after various image-related attacks.