Blind audio watermarking for medical data authentication using fractional Charlier transform and adaptive dithered quantization index modulation

Secure authentication and traceability of medical audio data remain critical challenges in modern telemedicine systems and digital health record management.. This paper proposes a novel blind and robust audio watermarking scheme for medical applications. The method combines the Fractional Charlier Transform (FrCT) for optimized time–frequency decomposition, local entropy analysis with critical-band masking for intelligent coefficient selection, and adaptive dithered quantization index modulation (ADQIM) for imperceptible watermark embedding. The proposed scheme provides comprehensive encryption of metadata including patient information and acquisition context through AES-based cryptographic mechanisms, while maintaining imperceptibility and embedding robustness. Comprehensive experimental validation on a diverse medical audio corpus demonstrates that the method achieves a practical payload capacity of 71.8 bits per second, high audio transparency with an SNR of 38.2 dB and a PESQ score of 4.15, and strong resilience against various signal processing attacks with an average BER of 3.2%. The approach provides a computationally efficient solution suitable for integration into operational telemedicine platforms and large-scale medical archiving systems, offering reliable authentication and integrity verification of medical audio records.