A New Subject-Sensitive Hashing Algorithm Based on Multi-PatchDrop and Swin-Unet for the Integrity Authentication of HRRS Image

• Published in: ISPRS international journal of geo-information Vol. 13; no. 9; p. 336
• Main Authors: Ding, Kaimeng, Wang, Yingying, Wang, Chishe, Ma, Ji
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2024
• Subjects: Algorithms; Computational linguistics; deep learning; dropouts; exhibitions; Hash based algorithms; image analysis; Image resolution; Image retrieval; Integrity; integrity authentication; Language processing; Multi-PatchDrop; Natural language interfaces; Neural networks; patch dropout; Performance enhancement; Remote sensing; Robustness; subject-sensitive hashing; tampering; Transformers; China
• DOI: 10.3390/ijgi13090336
• ISSN: 2220-9964

Transformer-based subject-sensitive hashing algorithms exhibit good integrity authentication performance and have the potential to ensure the authenticity and convenience of high-resolution remote sensing (HRRS) images. However, the robustness of Transformer-based subject-sensitive hashing is still not ideal. In this paper, we propose a Multi-PatchDrop mechanism to improve the performance of Transformer-based subject-sensitive hashing. The Multi-PatchDrop mechanism determines different patch dropout values for different Transformer blocks in ViT models. On the basis of a Multi-PatchDrop, we propose an improved Swin-Unet for implementing subject-sensitive hashing. In this improved Swin-Unet, Multi-PatchDrop has been integrated, and each Swin Transformer block (except the first one) is preceded by a patch dropout layer. Experimental results demonstrate that the robustness of our proposed subject-sensitive hashing algorithm is not only stronger than that of the CNN-based algorithms but also stronger than that of Transformer-based algorithms. The tampering sensitivity is of the same intensity as the AGIM-net and M-net-based algorithms, stronger than other Transformer-based algorithms.