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<p>Securing and tracing medical audio data is crucial in telemedicine and digital archiving. This paper presents a blind and irreversible audio watermarking scheme designed to satisfy imperceptibility, robustness, and embedding capacity requirements for sensitive medical applications. The method integrates the Fractional Charlier Transform (FrCT) for adaptive time-frequency analysis, local entropy analysis with the Watson perceptual model for intelligent coefficient selection, and adaptive logarithmic quantization index modulation (LQIM) for embedding. It securely incorporates patient and acquisition metadata, ensuring confidentiality and integrity via cryptographic and error-correction techniques. Experiments demonstrate a payload of 67.3 bits per second, high audio transparency (SNR &gt; 36 dB, PESQ &gt; 4.0), and robustness against various signal processing attacks (average BER 4.5%). The approach is computationally efficient and suitable for telemedicine workflows, supporting authentication, integrity verification, and traceability of medical audio records.</p>

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Audio watermarking for medical traceability based on local entropy and perceptual modeling

  • 期刊论文

  • 发表日期：

    2026年5月7日

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  • Salah Euschi,
  • Narima Zermi,
  • Med Sayah Moad,
  • Amine Khaldi,
  • Mohamed Redouane Kafi,
  • Aditya Kumar Sahu,
  • Narimene Mimoune

摘要

Securing and tracing medical audio data is crucial in telemedicine and digital archiving. This paper presents a blind and irreversible audio watermarking scheme designed to satisfy imperceptibility, robustness, and embedding capacity requirements for sensitive medical applications. The method integrates the Fractional Charlier Transform (FrCT) for adaptive time-frequency analysis, local entropy analysis with the Watson perceptual model for intelligent coefficient selection, and adaptive logarithmic quantization index modulation (LQIM) for embedding. It securely incorporates patient and acquisition metadata, ensuring confidentiality and integrity via cryptographic and error-correction techniques. Experiments demonstrate a payload of 67.3 bits per second, high audio transparency (SNR > 36 dB, PESQ > 4.0), and robustness against various signal processing attacks (average BER 4.5%). The approach is computationally efficient and suitable for telemedicine workflows, supporting authentication, integrity verification, and traceability of medical audio records.