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<p>Medical image authentication plays a vital role in secure healthcare industries, where assuring the integrity and authenticity of diagnostic images is critical for safe clinical decisions. This study presents a robust, dual watermarking framework that embeds a machine-readable QR code and a hospital logo into medical images using a hybrid frequency-domain method combining Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT). A lightweight Convolutional Neural Network (CNN) decoder is developed for efficient watermark extraction, optimized through a novel enhanced loss function that integrates Mean Squared Error (MSE), Structural Similarity Index (SSIM), and Sobel edge loss. The encoder-decoder framework ensures imperceptibility, low computational cost, and resilience to standard signal and geometric attacks. The model is tested against Salt &amp; Pepper noise, median filtering, rotation, and cropping to validate robustness. The proposed scheme achieves high watermark extraction fidelity with a Peak Signal-to-Noise Ratio (PSNR) ranging from 64.87 to 68.75 dB and Normalized Correlation (NC) values consistently reaching 1.0 under several attacks, demonstrating an average improvement of 28–35% in PSNR and 12–15% in NC. Furthermore, the lightweight CNN demonstrates a small model size of 0.65&#xa0;MB with real-time inference capability, making it suitable for embedded and resource-constrained medical devices. The results confirm that the proposed dual watermarking method maintains visual quality, structural integrity, and security of medical images while ensuring efficient and accurate watermark retrieval.</p>

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Lightweight dual-watermarking framework for medical image authentication and integrity preservation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月3日

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  • Rizwan Taj,
  • Saima Kanwal,
  • Amjad Alsirhani,
  • Faeiz Alserhani,
  • Rayan Alanazi,
  • Arslan Ali

摘要

Medical image authentication plays a vital role in secure healthcare industries, where assuring the integrity and authenticity of diagnostic images is critical for safe clinical decisions. This study presents a robust, dual watermarking framework that embeds a machine-readable QR code and a hospital logo into medical images using a hybrid frequency-domain method combining Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT). A lightweight Convolutional Neural Network (CNN) decoder is developed for efficient watermark extraction, optimized through a novel enhanced loss function that integrates Mean Squared Error (MSE), Structural Similarity Index (SSIM), and Sobel edge loss. The encoder-decoder framework ensures imperceptibility, low computational cost, and resilience to standard signal and geometric attacks. The model is tested against Salt & Pepper noise, median filtering, rotation, and cropping to validate robustness. The proposed scheme achieves high watermark extraction fidelity with a Peak Signal-to-Noise Ratio (PSNR) ranging from 64.87 to 68.75 dB and Normalized Correlation (NC) values consistently reaching 1.0 under several attacks, demonstrating an average improvement of 28–35% in PSNR and 12–15% in NC. Furthermore, the lightweight CNN demonstrates a small model size of 0.65 MB with real-time inference capability, making it suitable for embedded and resource-constrained medical devices. The results confirm that the proposed dual watermarking method maintains visual quality, structural integrity, and security of medical images while ensuring efficient and accurate watermark retrieval.