Blind Digital Watermarking Framework Based on DTCWT and NSCT for Telemedicine Application

Abstract

In this work, a blind watermarking framework for medical images based on Dual-Tree Complex Wavelet Transform (DTCWT) and Non-subsampled Contourlet Transform (NSCT) is proposed. The core idea of this technique is to embed the watermark in the appropriate NSCT sub-band obtained by decomposing the cover image low coefficients purchased from the DTCWT standing on a quantization embedding function, the extraction phase is done without the requirement of the original cover image, what makes it a fully blind process. As clarity and integrity of the retrieved watermark are mandatory, a series of tests were exerted to affirm the robustness of the proposed scheme. The effectiveness of the watermarking is validated by using the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) through experiments. Simulation results demonstrate the invisibility of the proposed method and its strong robustness against various attacks, including additive noise, image filtering, JPEG compression, amplitude scaling, rotation attack, and combinational attack. Furthermore, the method in hands outperformance within the quantitative comparisons with other techniques in the literature in terms of rapid execution time, and quality extraction of hidden information, and appropriateness to be integrated for secure exchange in the healthcare sector.

Zero Watermarking Algorithm for Medical Image Based on Resnet50-DCT

“…Therefore, the protection of medical images are very important. Digital watermarking is an effective means of information protection, which can be used to protect medical images [2,3].…”

An Improved Medical Image Watermarking Technique Based on Weber’s Law Descriptors

“…This reduces the computing cost, but weakens robustness. In the frequency domain, the cover image is first converted from the spatial domain to the frequency domain through various transforms, such as discrete Fourier transform (DFT) [10], discrete cosine transform (DCT) [11], discrete wavelet transform (DWT) [12][13][14], and singular value decomposition (SVD) [15]. Frequency-domain watermarking is more computationally intensive but more robust than spatial-domain watermarking.…”