FLOPBONSNet: ROI-Preserved Deep Watermarking for Secure Medical Image Authentication

Abstract

In the context of medical image storage, cloud-based exchange of digital images, and telemedicine transmission, the medical image watermarking technique is a crucial tool for verifying ownership, integrity, and authenticity. Most medical watermarking schemes currently in existence have its drawbacks, such as fixed-strength embedding, weak protection for important diagnostic regions, weak geometric robustness, and poor implementation reproducibility. In this paper, FLOPBONS-Net is proposed as an ROI-preserved hybrid watermarking framework, which is based on fuzzy logic, flower pollination optimization, DWT-DCT-SVD transform embedding, BCH/CRC payload protection, and attack-aware convolutional neural network decoder. The proposed method classifies ROI and NROI domain and calculates texture, edge, contrast and ROI-risk maps, and finally applies a fuzzy inference system to assign clinically safe embedding strength. A flower pollination optimizer is used to find optimal candidate blocks, embedding gains and transform coefficients with a multi-objective cost function, that optimally minimizes global distortion, ROI distortion, extraction error and maximizes payload and robustness. The watermark payload is scrambled and protected using BCH error correction and CRC verification. Noise, compression, cropping, rotation, blur, filtering, resizing, gamma correction and histogram equalization are used to train an attack-aware CNN decoder. Under major attacks, PSNR more than 50 dB, SSIM more than 0.99, and normalized correlation more than 0.91 are achieved in the evaluation of public chest radiography datasets. The manuscript contains links to datasets of Manusia, implementation architecture, formal equations, numbered algorithms, real-image figure slots with image identification command, visual attack panels, ablation analysis, statistical validation, runtime/memory analysis, scalability, and security metrics. Therefore, the proposed framework will be appropriate for secure radiology image exchange for both authentication and diagnostic quality.