PROVIMAPS: A Framework for Medical Image Data Provenance and Integrity in Medical Cyber-Physical Systems

This study presents PROVIMAPS, a framework for ensuring medical image data provenance and integrity in connected healthcare environments. The proposed approach introduces a software-based Device Fingerprint (DFP) generation method using intrinsic device parameters and SHA-256 hashing to uniquely identify the imaging source. The DFP is securely embedded into medical images using a DWT-based hybrid (DWT–DCT–SVD) watermarking technique, selected after a comparative analysis for its superior PSNR and SSIM performance across various medical image datasets. Additionally, an Image Average Intensity Profile (IAIP) is analyzed to detect image tampering and fused with the DFP for enhanced robustness. The combined signature ensures both device identification and content integrity. Comprehensive evaluation under various image attacks demonstrates the method's high robustness, computational efficiency, and resilience against manipulation. The PROVIMAPS framework offers a secure, low-cost, and scalable solution for maintaining trust in telemedicine, Internet of Medical Things (IoMT), eHealth, and Medical Cyber-Physical Systems (MCPS) applications, enabling reliable verification of image authenticity and source in distributed healthcare settings.