Deep Subject-Sensitive Hashing Network for High-Resolution Remote Sensing Image Integrity Authentication
Dingjie Xu, Sheng Quan Chen, Changqing Zhu, Hui Li, Luanyun Hu, Na Ren · IEEE Geoscience and Remote Sensing Letters · 2024
For ensuring the integrity of high-resolution remote sensing (HRRS) images, the perceptual hash method offers a dual advantage: it preserves the non-destructive 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 paper 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. Experimental results reveal that the algorithm achieves the best tampering detection performance, with top AUC (0.994) and leading precision and recall rates.