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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Remote sensing (Basel, Switzerland) Ročník 13; číslo 24; s. 5109
Hlavní autoři: Ding, Kaimeng, Chen, Shiping, Wang, Yu, Liu, Yueming, Zeng, Yue, Tian, Jin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.12.2021
Témata:
Algorithms
Asymmetric structures
Cryptography
Data compression
Deep learning
Digital watermarks
exhibitions
Hash based algorithms
image analysis
integrity authentication
Machine learning
Multimedia
perceptual hash
Remote sensing
Robustness
security of remote sensing images
subject-sensitive hashing
tampering
U-Net
ISSN:2072-4292, 2072-4292
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2072-4292
2072-4292
DOI:10.3390/rs13245109