A novel blind medical image watermarking scheme In NSST domain based on schur decomposition and DWT

Han Gao; Weimin Zheng

doi:10.1117/12.3006150

10 October 2023 A novel blind medical image watermarking scheme In NSST domain based on schur decomposition and DWT

Han Gao, Weimin Zheng

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 127994Z (2023) https://doi.org/10.1117/12.3006150
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

Nowadays, medical images face numerous information security challenges. On the one hand, medical images should be associated with patient information while ensuring data security and privacy protection. On the other hand, with the increasing use of technologies such as deep learning in medical images, there is a growing concern about copyright protection for medical image data used for sharing and training. Digital watermarking technology presents a potential solution to address these issues. This paper proposes a medical image watermarking scheme that utilizes Schur decomposition in combination with non-subsampled shearlet transform (NSST), discrete wavelet transform (DWT) and region of interest (ROI) identification for minimizing modifications to the original image. Furthermore, our proposed scheme does not require the original cover image for watermark extraction, reducing the reliance on additional information. The proposed method has been tested under various type of attacks, and the results have shown that it has high robustness and imperceptibility.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Han Gao and Weimin Zheng "A novel blind medical image watermarking scheme In NSST domain based on schur decomposition and DWT", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 127994Z (10 October 2023); https://doi.org/10.1117/12.3006150

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