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

• Published in: IEEE geoscience and remote sensing letters Vol. 21; pp. 1 - 5
• Main Authors: Xu, Dingjie, Chen, Sheng, Zhu, Changqing, Li, Hui, Hu, Luanyun, Ren, Na
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 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
• DOI: 10.1109/LGRS.2024.3407101

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.