| Autor: |
Kaimeng Ding, Shiping Chen, Yue Zeng, Yanan Liu, Bei Xu, Yingying Wang |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 3836-3849 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2151-1535 |
| DOI: |
10.1109/JSTARS.2024.3356660 |
| Popis: |
As a new integrity authentication technology, subject-sensitive hashing has the ability to achieve subject-sensitive authentication for high-resolution remote sensing (HRRS) images and can provide a security guarantee for their subsequent use. However, existing research on subject-sensitive hashing focuses on improving the structure of the deep neural network of the algorithm to improve the algorithm's performance, which makes it necessary to reconstruct the training dataset or modify the network structure in the face of different integrity authentication requirements. In this article, we delve into the impact of dropout on subject-sensitive hashing and propose a stepwise-drop mechanism to address the robustness and tampering-sensitivity requirements of subject-sensitive hashing. On this basis, a network named stepwise-drop and transformer-based U-net (SDTU-net) is proposed for subject-sensitive hashing of HRRS images. SDTU-net can use our proposed stepwise-drop mechanism to determine the drop rate of different network layers, which makes it possible to adjust the algorithm performance without changing network structure and training data. Experiments show that our SDTU-net based subject-sensitive hashing has better overall performance compared with existing algorithms, especially at medium and low thresholds. Our approach solves the problem that the existing algorithms cannot balance robustness and tamper sensitivity at low thresholds. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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