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<p>The widespread use of medical imaging in telemedicine and EHRs demands robust watermarking that preserves diagnostic quality. Conventional spread spectrum methods, despite their robustness, are limited by shared secret keys, geometric vulnerabilities, and weak resilience to generative AI attacks. This paper proposes a spread spectrum discrete wavelet transform (DWT) watermarking framework for medical images, which uses a deep perceptual masking network (JNDnet) to place the watermark where it remains invisible, a lightweight CNN to adaptively select resilient sub‑bands, and a neural detector that replaces fixed‑threshold correlation for improved extraction accuracy while remaining blind. Experiments on three medical datasets show imperceptibility (PSNR &gt; 44 dB, SSIM &gt; 0.98) and robust performance against common and generative AI attacks, with bit error rates below 6%. Ablation studies confirm the contribution of each component, and computational efficiency supports real‑time clinical deployment.</p>

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A neural‑guided spread spectrum watermarking framework for diagnostic medical imaging

  • 期刊论文

  • 发表日期：

    2026年5月19日

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  • Ikram Hacini,
  • Med Sayah Moad,
  • Med Redouane Kafi,
  • Narima Zermi,
  • Amine Khaldi,
  • Akram Boukhamla,
  • Aditya Kumar Sahu

摘要

The widespread use of medical imaging in telemedicine and EHRs demands robust watermarking that preserves diagnostic quality. Conventional spread spectrum methods, despite their robustness, are limited by shared secret keys, geometric vulnerabilities, and weak resilience to generative AI attacks. This paper proposes a spread spectrum discrete wavelet transform (DWT) watermarking framework for medical images, which uses a deep perceptual masking network (JNDnet) to place the watermark where it remains invisible, a lightweight CNN to adaptively select resilient sub‑bands, and a neural detector that replaces fixed‑threshold correlation for improved extraction accuracy while remaining blind. Experiments on three medical datasets show imperceptibility (PSNR > 44 dB, SSIM > 0.98) and robust performance against common and generative AI attacks, with bit error rates below 6%. Ablation studies confirm the contribution of each component, and computational efficiency supports real‑time clinical deployment.