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<p>Protecting medical images in interconnected healthcare systems requires maintaining both diagnostic integrity and secure verification. This study presents a watermarking framework combining deep feature extraction, chaotic cryptography, adaptive frequency-domain embedding, and AI-assisted extraction. Deep convolutional networks identify perceptually tolerant regions for content-aware embedding while preserving vital diagnostic areas. Hybrid chaos-based encryption secures watermark data against unauthorized recovery. The embedding operates in the fractional discrete cosine transform (FDCT) domain with adaptive coefficient selection and multi-objective optimization balancing imperceptibility, robustness, and capacity. During extraction, a convolutional autoencoder refines recovered watermarks, maintaining fidelity under compression, noise, and geometric distortions. Experimental validation on medical datasets demonstrates high embedding capacity (0.07309 BPP), excellent visual similarity (PSNR = 46.85 dB, SSIM = 0.9995), and strong resilience against attacks (NCC ≥ 0.94), ensuring secure and compliant medical image transmission.</p>

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Frequency domain watermarking of medical images based on fractional discrete Cosine, Mellin, and Schur transforms

  • 期刊论文

  • 发表日期：

    2026年2月26日

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  • Naima Saadaoui,
  • Boukhamla Akram Zine Eddine,
  • Narima Zermi,
  • Amine Khaldi,
  • Mohamed Redouane Kafi,
  • Aditya Kumar Sahu

摘要

Protecting medical images in interconnected healthcare systems requires maintaining both diagnostic integrity and secure verification. This study presents a watermarking framework combining deep feature extraction, chaotic cryptography, adaptive frequency-domain embedding, and AI-assisted extraction. Deep convolutional networks identify perceptually tolerant regions for content-aware embedding while preserving vital diagnostic areas. Hybrid chaos-based encryption secures watermark data against unauthorized recovery. The embedding operates in the fractional discrete cosine transform (FDCT) domain with adaptive coefficient selection and multi-objective optimization balancing imperceptibility, robustness, and capacity. During extraction, a convolutional autoencoder refines recovered watermarks, maintaining fidelity under compression, noise, and geometric distortions. Experimental validation on medical datasets demonstrates high embedding capacity (0.07309 BPP), excellent visual similarity (PSNR = 46.85 dB, SSIM = 0.9995), and strong resilience against attacks (NCC ≥ 0.94), ensuring secure and compliant medical image transmission.