Dual medical image watermarking using SRU-enhanced network and EICC chaotic map
Abstract With the rapid advancement of next-generation information technology, smart healthcare has seamlessly integrated into various facets of people’s daily routines. Accordingly, enhancing the integrity and security of medical images has gained significant prominence as a crucial research trajec...
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| Format: | Article |
| Language: | English |
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Springer
2024-12-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-024-01723-6 |
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| author | Fei Yan Zeqian Wang Kaoru Hirota |
| author_facet | Fei Yan Zeqian Wang Kaoru Hirota |
| author_sort | Fei Yan |
| collection | DOAJ |
| description | Abstract With the rapid advancement of next-generation information technology, smart healthcare has seamlessly integrated into various facets of people’s daily routines. Accordingly, enhancing the integrity and security of medical images has gained significant prominence as a crucial research trajectory. In this study, a dual watermarking scheme based on SRU-ConvNeXt V2 (SCNeXt) model and exponential iterative-cubic-cosine (EICC) chaotic map is proposed for medical image integrity verification, tamper localization, and copyright protection. A logo image for integrity verification is embedded into the region of interest within the medical image, and a text image containing copyright information is combined with the feature vectors extracted by SCNeXt for generating zero-watermark information. The security of watermarks is strengthened through a pre-embedding encryption algorithm using the chaotic sequence produced by the EICC map. A comprehensive set of experiments was conducted to validate the proposed dual watermarking scheme. The results demonstrate that the scheme offers significant advantages in both imperceptibility and robustness over traditional methods, including those that rely on manual extraction of medical image features. The scheme achieves excellent imperceptibility, with an average PSNR of 52.29 dB and an average SSIM of 0.9962. Moreover, it displays strong resilience against various attacks, particularly high-strength common and geometric attacks, maintaining an NC value above 0.84, which confirms its robustness. These findings highlight the superiority of the proposed dual watermarking scheme, establishing its potential as an advanced solution for secure and reliable medical image management. |
| format | Article |
| id | doaj-art-d27b06cb25e9495693e175e01ff98faf |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-d27b06cb25e9495693e175e01ff98faf2025-02-02T12:49:31ZengSpringerComplex & Intelligent Systems2199-45362198-60532024-12-0111113110.1007/s40747-024-01723-6Dual medical image watermarking using SRU-enhanced network and EICC chaotic mapFei Yan0Zeqian Wang1Kaoru Hirota2School of Computer Science and Technology, Changchun University of Science and TechnologySchool of Computer Science and Technology, Changchun University of Science and TechnologySchool of Computing, Tokyo Institute of TechnologyAbstract With the rapid advancement of next-generation information technology, smart healthcare has seamlessly integrated into various facets of people’s daily routines. Accordingly, enhancing the integrity and security of medical images has gained significant prominence as a crucial research trajectory. In this study, a dual watermarking scheme based on SRU-ConvNeXt V2 (SCNeXt) model and exponential iterative-cubic-cosine (EICC) chaotic map is proposed for medical image integrity verification, tamper localization, and copyright protection. A logo image for integrity verification is embedded into the region of interest within the medical image, and a text image containing copyright information is combined with the feature vectors extracted by SCNeXt for generating zero-watermark information. The security of watermarks is strengthened through a pre-embedding encryption algorithm using the chaotic sequence produced by the EICC map. A comprehensive set of experiments was conducted to validate the proposed dual watermarking scheme. The results demonstrate that the scheme offers significant advantages in both imperceptibility and robustness over traditional methods, including those that rely on manual extraction of medical image features. The scheme achieves excellent imperceptibility, with an average PSNR of 52.29 dB and an average SSIM of 0.9962. Moreover, it displays strong resilience against various attacks, particularly high-strength common and geometric attacks, maintaining an NC value above 0.84, which confirms its robustness. These findings highlight the superiority of the proposed dual watermarking scheme, establishing its potential as an advanced solution for secure and reliable medical image management.https://doi.org/10.1007/s40747-024-01723-6Smart healthcareMedical imageDual watermarkingChaotic mapConvolutional neural network |
| spellingShingle | Fei Yan Zeqian Wang Kaoru Hirota Dual medical image watermarking using SRU-enhanced network and EICC chaotic map Complex & Intelligent Systems Smart healthcare Medical image Dual watermarking Chaotic map Convolutional neural network |
| title | Dual medical image watermarking using SRU-enhanced network and EICC chaotic map |
| title_full | Dual medical image watermarking using SRU-enhanced network and EICC chaotic map |
| title_fullStr | Dual medical image watermarking using SRU-enhanced network and EICC chaotic map |
| title_full_unstemmed | Dual medical image watermarking using SRU-enhanced network and EICC chaotic map |
| title_short | Dual medical image watermarking using SRU-enhanced network and EICC chaotic map |
| title_sort | dual medical image watermarking using sru enhanced network and eicc chaotic map |
| topic | Smart healthcare Medical image Dual watermarking Chaotic map Convolutional neural network |
| url | https://doi.org/10.1007/s40747-024-01723-6 |
| work_keys_str_mv | AT feiyan dualmedicalimagewatermarkingusingsruenhancednetworkandeiccchaoticmap AT zeqianwang dualmedicalimagewatermarkingusingsruenhancednetworkandeiccchaoticmap AT kaoruhirota dualmedicalimagewatermarkingusingsruenhancednetworkandeiccchaoticmap |