# A Novel Perceptual Hash Algorithm for Multispectral Image Authentication
## 1 Introduction  
Due to the rapid growth of remote sensing, multispectral (MS) remote sensing images have exhibited increasing potential for more and more applications ranging from independent land mapping services to government and military activities. However, with the development of image processing and network transmission techniques, it has become easier to tamper with or forge multispectral remote sensing images during the process of processing and transmission. For example, the widespread use of sophisticated image editing tools can make the authenticity and integrity of MS images suffer from a serious threat, even rendering the multispectral images useless. Therefore, ensuring the content integrity of the MS image is a major issue before the multispectral image can be used. A perceptual hash algorithm, also known as a robustness hash algorithm, is able to solve the problems of MS image content authentication, while the classic cryptographic authentication algorithms, such as MD5 and SHA1, are not suitable for this purpose since they are sensitive to each bit of the input image. A perceptual hash algorithm maps an input image into a compatible feature vector called perceptual hash value, which is a short summary of an image's perceptual content. Perceptual hash algorithms have been developed as a frontier research topic in the field of digital media content security, and they can be applied for image content authentication, image retrieval, image registration, and digital watermarking. Similar to cryptographic hash functions, the perceptual hash algorithmcompresses the representation of the perceptual features of an image to generate a fixed-length sequence, which ensures that perceptually similar images produce similar sequences [1]. In recent years, a number of perceptual hash algorithms have been proposed to meet the requirements of different types of data [2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15]. [PERSON] et al. [2] propose a perceptual hash algorithm for image authentication, which uses a secret key to randomly modulate image pixels to create a transformed feature space. It offers good robustness and it can detect minute tampering with localization of the tampered area, but it is not able be applied to an MS image with many more bands. [PERSON] et al. [3] propose a novel perceptual image hash algorithm based on block truncation coding. [PERSON] et al. [4] develop a perceptual hash based on compressive sensing and Fourier-Mellin transformation. The proposed method is robust to a wide range of distortions and attacks, and it yields better identification performances under geometric attacks such as rotation attacks and brightness changes. [PERSON] et al. [5] propose a multi-scale image hashing method by using the location-context information of the features generated by adaptive and local feature extraction techniques. [PERSON] et al. [6] propose a hash algorithm for 3D images by selecting suitable Dual-tree complex wavelet transform coefficients to form the final hash sequence. [PERSON] et al. [7] propose a perceptual hash algorithm for images based on salient structure features, which can be applied in image authentication and retrieval. [PERSON] et al. [8] propose a perceptual audio hash algorithm based on maximum-likelihood watermarking detection, which can be applied in a content-based search. [PERSON] et al. [9] propose a wave atom transform (WAT) based image hash algorithm using distributed source coding to reduce the size of hash code, providing a better performance than existing WAT. [PERSON] et al. [10] propose a perceptual hash algorithm with innovative use of discrete cosine transform and local linear embedding, which can be used in image authentication, image retrieval, copy detection and image forensics. [PERSON] et al. [11] propose a video hash model based on a deep belief network, which generates the video hash from visual attention features. [PERSON] et al.