Transformer-Based Subject-Sensitive Hashing for Integrity Authentication of High-Resolution Remote Sensing (HRRS) Images

Abstract

The implicit prerequisite for using HRRS images is that the images can be trusted. Otherwise, their value would be greatly reduced. As a new data security technology, subject-sensitive hashing overcomes the shortcomings of existing integrity authentication methods and could realize subject-sensitive authentication of HRRS images. However, shortcomings of the existing algorithm, in terms of robustness, limit its application. For example, the lack of robustness against JPEG compression makes existing algorithms more passive in some applications. To enhance the robustness, we proposed a Transformer-based subject-sensitive hashing algorithm. In this paper, first, we designed a Transformer-based HRRS image feature extraction network by improving Swin-Unet. Next, subject-sensitive features of HRRS images were extracted by this improved Swin-Unet. Then, the hash sequence was generated through a feature coding method that combined mapping mechanisms with principal component analysis (PCA). Our experimental results showed that the robustness of the proposed algorithm was greatly improved in comparison with existing algorithms, especially the robustness against JPEG compression.

Keywords

• deep learning
• HRRS images
• subject-sensitive hashing
• transformer
• U-net
• perceptual hashing