Deep Subject-Sensitive Hashing Network for High-Resolution Remote Sensing Image Integrity Authentication

For ensuring the integrity of high-resolution remote sensing (HRRS) images, the perceptual hash method offers a dual advantage. It preserves the nondestructive nature of the original image while also ensuring robustness to content-preserving operations. However, current deep learning-based HRRS imag...

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Vydáno v:IEEE geoscience and remote sensing letters Ročník 21; s. 1 - 5
Hlavní autoři: Xu, Dingjie, Chen, Sheng, Zhu, Changqing, Li, Hui, Hu, Luanyun, Ren, Na
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Authentication
CBAM
Construction
Deep learning
Feature extraction
High resolution
high-resolution remote sensing (HRRS) image
Image coding
Image resolution
Integrity
integrity authentication
Machine learning
Remote sensing
Robustness
Sensitivity
subject-sensitive hashing
Training
ISSN:1545-598X, 1558-0571
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Shrnutí:For ensuring the integrity of high-resolution remote sensing (HRRS) images, the perceptual hash method offers a dual advantage. It preserves the nondestructive nature of the original image while also ensuring robustness to content-preserving operations. However, current deep learning-based HRRS image hashing methods for integrity authentication are notably limited as they terminate at the feature extraction stage and fail to achieve an end-to-end construction from image to hash value. Consequently, there is a looming risk of uncontrollability and unexpected events. To overcome this problem, this letter proposes a deep subject-sensitive hashing network (DSSHN), presenting a unified network for end-to-end feature extraction and hash construction. Improved convolutional block attention module (I-CBAM) helps the network to focus more on subject-sensitive features. A targeted training scheme ensures perceptual hash robustness. The experimental results reveal that the algorithm achieves the best tampering detection performance, with top AUC (0.994) and leading precision and recall rates.
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ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2024.3407101