Abstract
In recent years, digital healthcare and deep learning have seen substantial advancements, with medical imaging emerging as a crucial component. Despite this progress, the security of these images remains a significant challenge, particularly during network transmission where they are at risk of unauthorized access and tampering. To mitigate these risks, we introduce a novel and robust watermarking algorithm, rooted in advanced deep learning technologies and tailored specifically for medical images. Employing a custom Generative Adversarial Network (GAN), our innovative approach embeds QR codes—encoding confidential, patient-specific data—directly into medical images. Rigorous experimental evaluations confirm the resilience of our solution against a wide array of adversarial attacks and various image distortions, achieving an exceptional average Peak Signal-to-Noise Ratio (PSNR) of 37.12 and an extraction accuracy above 99%. Our algorithm not only enhances the security and integrity of medical images but also fortifies the protection of patient privacy. Importantly, this work fills a research gap by applying Generative Adversarial Networks (GANs) to the domain of end-to-end medical image watermarking.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Anand, A., Singh, A.K.: An improved DWT-SVD domain watermarking for medical information security. Comput. Commun. 152, 72–80 (2020)
Anand, A., Singh, A.K.: Watermarking techniques for medical data authentication: a survey. Multimed. Tools Appl. 80, 30165–30197 (2021)
Balasamy, K., Suganyadevi, S.: A fuzzy based ROI selection for encryption and watermarking in medical image using DWT and SVD. Multimed. Tools Appl. 80(5), 7167–7186 (2021)
Boujerfaoui, S., Riad, R., Douzi, H., Ros, F., Harba, R.: Image watermarking between conventional and learning-based techniques: a literature review. Electronics 12(1), 74 (2022)
Cedillo-Hernandez, M., Garcia-Ugalde, F., Nakano-Miyatake, M., Perez-Meana, H.: Robust watermarking method in DFT domain for effective management of medical imaging. SIViP 9, 1163–1178 (2015)
El-Saadawy, A., El-Sayed, A., ElBery, M., Roushdy, M.I.: Medical images watermarking schemes-a review. Int. J. Intell. Comput. Inf. Sci. 21(1), 119–131 (2021)
Gong, C., Liu, J., Gong, M., Li, J., Bhatti, U.A., Ma, J.: Robust medical zero-watermarking algorithm based on residual-densenet. IET Biometrics 11(6), 547–556 (2022)
Goodfellow, I., et al.: Generative adversarial nets. In: Neural Information Processing Systems (2014)
Gull, S., Parah, S.A.: Advances in medical image watermarking: a state of the art review. Multimed. Tools Appl. 1–41 (2023)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
Kahlessenane, F., Khaldi, A., Kafi, R., Euschi, S.: A DWT based watermarking approach for medical image protection. J. Ambient. Intell. Humaniz. Comput. 12(2), 2931–2938 (2021)
Kelkar, V., Tuckley, K., Nemade, H., et al.: Novel variants of a histogram shift-based reversible watermarking technique for medical images to improve hiding capacity. J. Healthcare Eng. 2017 (2017)
Kermany, D., Zhang, K., Goldbaum, M., et al.: Labeled optical coherence tomography (OCT) and chest X-ray images for classification. Mendeley Data 2(2), 651 (2018)
Kumar, C., Singh, A.K., Kumar, P.: Improved wavelet-based image watermarking through SPIHT. Multimed. Tools Appl. 79(15–16), 11069–11082 (2020)
Liu, X., Lou, J., Wang, Y., Du, J., Zou, B., Chen, Y.: Discriminative and robust zero-watermarking scheme based on completed local binary pattern for authentication and copyright identification of medical images. In: Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications, vol. 10579, pp. 381–389. SPIE (2018)
Mehta, R., Rajpal, N., Vishwakarma, V.P.: A robust and efficient image watermarking scheme based on Lagrangian SVR and lifting wavelet transform. Int. J. Mach. Learn. Cybern. 8, 379–395 (2017)
Ogundokun, R.O., Abikoye, O.C.: A safe and secured medical textual information using an improved LSB image steganography. Int. J. Digit. Multimed. Broadcasting 2021, 1–8 (2021)
Rguibi, Z., Abdelmajid, H., Zitouni, D.: Deep learning in medical imaging: a review, pp. 131–144 (2022). https://doi.org/10.1201/9781003269793-15
Su, Q., et al.: New rapid and robust color image watermarking technique in spatial domain. IEEE Access 7, 30398–30409 (2019). https://doi.org/10.1109/ACCESS.2019.2895062
Swaraja, K., Meenakshi, K., Kora, P.: An optimized blind dual medical image watermarking framework for tamper localization and content authentication in secured telemedicine. Biomed. Signal Process. Control 55, 101665 (2020)
Thakur, S., Singh, A.K., Ghrera, S.P., Elhoseny, M.: Multi-layer security of medical data through watermarking and chaotic encryption for tele-health applications. Multimed. Tools Appl. 78, 3457–3470 (2019)
Vaidya, S.P.: Fingerprint-based robust medical image watermarking in hybrid transform. Vis. Comput. 39(6), 2245–2260 (2023)
Yang, C., Li, J., Bhatti, U.A., Liu, J., Ma, J., Huang, M.: Robust zero watermarking algorithm for medical images based on Zernike-DCT. Secur. Commun. Netw. 2021, 1–8 (2021)
Zermi, N., Khaldi, A., Kafi, M.R., Kahlessenane, F., Euschi, S.: Robust SVD-based schemes for medical image watermarking. Microprocess. Microsyst. 84, 104134 (2021)
Acknowledgments
The work was supported by the Natural Science Foundation of Fujian Province of China (No. 2022J01003).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, K., Gao, C., Yang, S. (2024). A Custom GAN-Based Robust Algorithm for Medical Image Watermarking. In: Rudinac, S., et al. MultiMedia Modeling. MMM 2024. Lecture Notes in Computer Science, vol 14554. Springer, Cham. https://doi.org/10.1007/978-3-031-53305-1_33
Download citation
DOI: https://doi.org/10.1007/978-3-031-53305-1_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-53304-4
Online ISBN: 978-3-031-53305-1
eBook Packages: Computer ScienceComputer Science (R0)