The increasing digitization of data offers significant advantages, particularly in digital images, which continue to grow rapidly due to advances in image acqui sition systems. However, this transition also presents significant security risks, necessitating robust methods to protect digital images from illegal manipulation. While existing watermarking approaches for medical imaging often struggle to balance robustness, computational efficiency, and diagnostic integrity, this study introduces a robust blind watermarking technique that integrates cryptographic hashing, deep learning, and frequency-domain embedding to address these lim itations. The methodology leverages the Haar wavelet transform to embed a binary watermark, generated from personal information using the secure hashing algorithm SHA-256, into the HH2 sub-band of the red channel. A convolutional neural network-based model optimized watermark insertion and facilitated blind extraction. Comprehensive evaluations across diverse medical imaging modalities, including MRI, ultrasound, and CT scans, demonstrate excellent performance with peak signal-to-noise ratio (PSNR) values exceeding 41 dB achieving up to 43.13 dB for fundus images, representing a 3.1% improvement over the best exist ing method and structural similarity (SSIM) values around 0.94. The proposed method shows significant speed improvements of approximately 75% compared to state-of-the-art techniques, with embedding and extraction times of 0.4430 sec onds and 0.1800 seconds, respectively, compared to 1.5 seconds total processing time of recent deep learning methods. These findings underscore the technique’s potential for telemedicine applications, where maintaining high image quality and security is paramount for accurate diagnosis.
Hamami et al. (Mon,) studied this question.