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 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.