A robust NIfTI image authentication framework to ensure reliable and safe diagnosis

View article
PeerJ Computer Science

Main article text

 

Introduction

  1. It is designed explicitly for NIfTI images, which are commonly used in medical imaging but have not been extensively studied in watermarking.

  2. It uses LWT decomposition to extract precise information from the image, allowing optimal modification during watermark embedding.

  3. It applies HD to the LL sub-band, the low-frequency portion of the image, ensuring that the watermark is embedded in an area less likely to be affected by noise or other distortions.

  4. It uses an affine transform to insert the watermark into the upper triangular matrix of the Hessenberg matrix decomposition, which provides a highly secure and robust watermarking technique.

  5. The proposed method is evaluated using objective metrics such as PSNR, SSIM, and NC, demonstrating its effectiveness in watermark invisibility and attack robustness.

Background theory

Slantlet transform

Hessenberg matrix decomposition

the higher Hessenberg matrix H is multiplied by the orthogonal matrix Q. More specifically, the H matrix takes the following shape:

Affine transformation

Proposed watermarking scheme

Watermark embedding algorithm

Watermark extraction algorithm

where, H and H represent the Hessenberg matrix of the watermarked slice as well as the host slice respectively. μ has been used to establish the embedding factor, W is used to represent the extracted component in an encrypted form.

Experiment results and analysis

Performance analysis measures

Results and discussion

Robustness analysis

Conclusion

Additional Information and Declarations

Competing Interests

The authors declare that they have no competing interests.

Author Contributions

Shakila Basheer conceived and designed the experiments, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Kamred Udham Singh conceived and designed the experiments, performed the experiments, performed the computation work, authored or reviewed drafts of the article, and approved the final draft.

Vandana Sharma analyzed the data, prepared figures and/or tables, and approved the final draft.

Surbhi Bhatia conceived and designed the experiments, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Nilesh Pande analyzed the data, prepared figures and/or tables, and approved the final draft.

Ankit Kumar conceived and designed the experiments, performed the experiments, performed the computation work, authored or reviewed drafts of the article, and approved the final draft.

Data Availability

The following information was supplied regarding data availability:

The code and data are available at Github and Zenodo: https://github.com/kamredudhamsingh/Hess_watermarking;

Ma Jun, Ge Cheng, Wang Yixin, An Xingle, Gao Jiantao, Yu Ziqi, Zhang Minqing, Liu Xin, Deng Xueyuan, Cao Shucheng, Wei Hao, Mei Sen, Yang Xiaoyu, Nie Ziwei, Li Chen, Tian Lu, Zhu Yuntao, Zhu Qiongjie, Dong Guoqiang, & He Jian. (2020). COVID-19 CT Lung and Infection Segmentation Dataset (Verson 1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3757476

Funding

This research is supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R195) Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

13 Citations 887 Views 73 Downloads

MIT

Your institution may have Open Access funds available for qualifying authors. See if you qualify

Publish for free

Comment on Articles or Preprints and we'll waive your author fee
Learn more

Five new journals in Chemistry

Free to publish • Peer-reviewed • From PeerJ
Find out more