## 2 Related Work  
### Perceptual Hash and HRRS Image  
Tamper sensitivity and robustness are the two most important attributes of perceptual hash, but the two are often contradictory. Compared with cryptographic hash function, the most prominent feature of perceptual hash is that it has perceptual robustness, that is, after the image is subjected to the content-preserving operations (such as format conversion, watermark embedding, etc. ),the corresponding perceptual hash value does not change significantly. Similar to cryptographic hash function, perceptual hashing can map multimedia data (such as image and video) into a sequence of digests of a certain length, that is, the content information of the multimedia data can be represented with as little information as possible. What is more, perceptual hash is sensitive to the changes or tampering of the multimedia data and can realize integrity authentication based on perceptual content. According to different multimedia data, perceptual hashing mainly includes image perceptual hashing, audio perceptual hashing, and video perceptual hashing. Perceptual hashing can play an effective role in many areas, such as image retrieval, image authentication, copy detection, etc. As the resolution of HRRS images continues to improve, the increasing complexity of HRRS images application and sharing environment, the integrity authentication of HRRS images are becoming more and more serious, and higher requirements are imposed on the perceptual hash authentication algorithm. Research on perceptual hash authentication algorithm focuses on the extraction method of image robust features and the effective dimensionality reduction coding method. In the research of perceptual hash for HRRS images, the key and difficult point is also how to extract the perceptual features that satisfy the integrity authentication requirements and generate a perceptual hash value that meets the actual needs. The existing perceptual hash algorithms based on the traditional feature extraction method still have some shortcomings in terms of robustness. For example, \"false features\" such as \"noise\" cannot be effectively identified. It is not possible to carry out targeted authentication for specific application environments. The most prominent feature of HRRS images is the high spatial resolution and strong discrimination of ground objects. High spatial resolution is a relative concept that will constantly change standards as technology evolves. At present, the highest spatial resolution of the world commercial remote sensing satellite image has reached the sub-meter level: The resolution of the international commercial remote sensing satellite GeoEye has reached 0.41 m, and the WorldView-3 satellite has a resolution of 0.3 m resolution. Compared with low- or moderate-resolution-resolution remote sensing images, HRRS images display more detailed information. The existing image perceptual hash algorithms do not take the data characteristics and application environment of HRRS images into account and cannot fully satisfy the perceptual-content-based authentication of HRRS images. As an important geospatial data, the high-resolution image has the following characteristics: High-resolution images have higher requirements for measurement accuracy, and image perceptual hash algorithms cannot meet the requirements of high-resolution images for measurement accuracy. The interpretation of HRRS image content has diversity and ambiguity, which increases the difficulty of the perceptual hash algorithm to extract the content features of HRRS images. Unlike medical images and ordinary optical images, HRRS images (especially the HRRS images with a large coverage) often have no unique subject information, which increases the uncertainty of the extraction of perceptual features of HRRS images. HRRS images also have other features such as scale and temporal, while ordinary images are not very obvious in this respect.