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 |
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| Jazyk: | angličtina |
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2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1545-598X, 1558-0571 |
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| Abstract | 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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| AbstractList | 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. |
| Author | Li, Hui Ren, Na Zhu, Changqing Hu, Luanyun Chen, Sheng Xu, Dingjie |
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| SubjectTerms | 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 |
| Title | Deep Subject-Sensitive Hashing Network for High-Resolution Remote Sensing Image Integrity Authentication |
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