A learned perceptual and neural detection framework for spread spectrum watermarking in medical images
Hacini Ikram, Moad Med Sayah, Kafi Redouane, Zermi Narima, Amine Khaldi, Akram Boukhamla, Aditya Kumar Sahu · Array · 2026
Medical image watermarking must ensure data authenticity and integrity without compromising diagnostic quality. Conventional spread spectrum methods suffer from key dependency, geometric vulnerability, hand-crafted perceptual models, and generative AI susceptibility. This paper presents a hybrid DWT-based spread spectrum framework for medical imaging with three innovations: a learned perceptual masking network (JND_net) that replaces analytical JND models, a lightweight CNN (SubBandSelector) that dynamically selects resilient wavelet sub-bands, and a neural detector (DetectorNet) that supplants fixed-threshold detection, all integrated with dual-key security (2 128 key space) and MAC authentication. Experiments on 1,200 medical images with radiologist validation (51.3% detection accuracy, chance level; diagnostic confidence unchanged, p = 0.34) show bit error rates below 0.03 under JPEG, noise, and filtering, outperforming seven baselines (p < 0.001). Under generative AI attacks, BER reaches 0.112 (diffusion) and 0.087 (inpainting). The framework operates in real time (3.7 ms on T4 GPU, 25 ms on CPU) under blind extraction.