AAU-Net: Attention-Based Asymmetric U-Net for Subject-Sensitive Hashing of Remote Sensing Images
The prerequisite for the use of remote sensing images is that their security must be guaranteed. As a special subset of perceptual hashing, subject-sensitive hashing overcomes the shortcomings of the existing perceptual hashing that cannot distinguish between “subject-related tampering” and “subject...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/24/5109 |
| _version_ | 1827669819272462336 |
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| author | Kaimeng Ding Shiping Chen Yu Wang Yueming Liu Yue Zeng Jin Tian |
| author_facet | Kaimeng Ding Shiping Chen Yu Wang Yueming Liu Yue Zeng Jin Tian |
| author_sort | Kaimeng Ding |
| collection | DOAJ |
| description | The prerequisite for the use of remote sensing images is that their security must be guaranteed. As a special subset of perceptual hashing, subject-sensitive hashing overcomes the shortcomings of the existing perceptual hashing that cannot distinguish between “subject-related tampering” and “subject-unrelated tampering” of remote sensing images. However, the existing subject-sensitive hashing still has a large deficiency in robustness. In this paper, we propose a novel attention-based asymmetric U-Net (AAU-Net) for the subject-sensitive hashing of remote sensing (RS) images. Our AAU-Net demonstrates obvious asymmetric structure characteristics, which is important to improve the robustness of features by combining the attention mechanism and the characteristics of subject-sensitive hashing. On the basis of AAU-Net, a subject-sensitive hashing algorithm is developed to integrate the features of various bands of RS images. Our experimental results show that our AAU-Net-based subject-sensitive hashing algorithm is more robust than the existing deep learning models such as Attention U-Net and MUM-Net, and its tampering sensitivity remains at the same level as that of Attention U-Net and MUM-Net. |
| first_indexed | 2024-03-10T03:11:24Z |
| format | Article |
| id | doaj.art-3190d12514f54976b897c96cf8d5392c |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T03:11:24Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-3190d12514f54976b897c96cf8d5392c2023-11-23T10:24:56ZengMDPI AGRemote Sensing2072-42922021-12-011324510910.3390/rs13245109AAU-Net: Attention-Based Asymmetric U-Net for Subject-Sensitive Hashing of Remote Sensing ImagesKaimeng Ding0Shiping Chen1Yu Wang2Yueming Liu3Yue Zeng4Jin Tian5Jinling Institute of Technology, Nanjing 211169, ChinaCSIRO Data61, Sydney, NSW 1710, AustraliaChangjiang Nanjing Waterway Bureau, Nanjing 210011, ChinaState Key Laboratory of Resource and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaJinling Institute of Technology, Nanjing 211169, ChinaJinling Institute of Technology, Nanjing 211169, ChinaThe prerequisite for the use of remote sensing images is that their security must be guaranteed. As a special subset of perceptual hashing, subject-sensitive hashing overcomes the shortcomings of the existing perceptual hashing that cannot distinguish between “subject-related tampering” and “subject-unrelated tampering” of remote sensing images. However, the existing subject-sensitive hashing still has a large deficiency in robustness. In this paper, we propose a novel attention-based asymmetric U-Net (AAU-Net) for the subject-sensitive hashing of remote sensing (RS) images. Our AAU-Net demonstrates obvious asymmetric structure characteristics, which is important to improve the robustness of features by combining the attention mechanism and the characteristics of subject-sensitive hashing. On the basis of AAU-Net, a subject-sensitive hashing algorithm is developed to integrate the features of various bands of RS images. Our experimental results show that our AAU-Net-based subject-sensitive hashing algorithm is more robust than the existing deep learning models such as Attention U-Net and MUM-Net, and its tampering sensitivity remains at the same level as that of Attention U-Net and MUM-Net.https://www.mdpi.com/2072-4292/13/24/5109security of remote sensing imagesdeep learningsubject-sensitive hashingintegrity authenticationperceptual hashU-Net |
| spellingShingle | Kaimeng Ding Shiping Chen Yu Wang Yueming Liu Yue Zeng Jin Tian AAU-Net: Attention-Based Asymmetric U-Net for Subject-Sensitive Hashing of Remote Sensing Images Remote Sensing security of remote sensing images deep learning subject-sensitive hashing integrity authentication perceptual hash U-Net |
| title | AAU-Net: Attention-Based Asymmetric U-Net for Subject-Sensitive Hashing of Remote Sensing Images |
| title_full | AAU-Net: Attention-Based Asymmetric U-Net for Subject-Sensitive Hashing of Remote Sensing Images |
| title_fullStr | AAU-Net: Attention-Based Asymmetric U-Net for Subject-Sensitive Hashing of Remote Sensing Images |
| title_full_unstemmed | AAU-Net: Attention-Based Asymmetric U-Net for Subject-Sensitive Hashing of Remote Sensing Images |
| title_short | AAU-Net: Attention-Based Asymmetric U-Net for Subject-Sensitive Hashing of Remote Sensing Images |
| title_sort | aau net attention based asymmetric u net for subject sensitive hashing of remote sensing images |
| topic | security of remote sensing images deep learning subject-sensitive hashing integrity authentication perceptual hash U-Net |
| url | https://www.mdpi.com/2072-4292/13/24/5109 |
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