# A Novel Perceptual Hash Algorithm for Multispectral Image Authentication
## 1 Introduction
[13] propose a Discrete Cosine Transform (DCT) based perceptual hash scheme to track vehicle candidates and achieve a high level of robustness. [PERSON] et al. [14] exploit the circle-based and the block-based strategies to extract the structural features. [PERSON] et al. [15] adopt a gradient-based perceptual hash algorithm to encode invariant macro structures of person images to make the representation robust to both illumination and viewpoint changes. However, only a few researches on the perceptual hashing for multispectral remote sensing image have been carried out. The existing perceptual hash algorithms do not take the characteristics of MS images into consideration and they are not suitable for MS images. Therefore, new perceptual hash algorithms need to be introduced to solve the problems of MS image authentication. Different from MS remote sensing images, panchromatic (PAN) remote sensing images with the characteristics of high spatial resolution and low spectral resolution can be authenticated by the existing perceptual hash algorithms for normal images, as the digital form of PAN images is the same as normal images. In contrast, an MS remote sensing image is characterized by lower spatial resolution than PAN, but higher spectral resolution, which makes the existing perceptual hash algorithms for imaging unsuitable for multispectral remote sensing image authentication. An MS image obtains information from the visible and the near-infrared spectrum of reflected light; it is composed of a set of (more than three) monochrome images of the same scene, each of which is referred to as a band and is taken at a specific wavelength. Whereas the normal color image is composed of only three monochrome images, the PAN image has only one band. The bands of the MS remote sensing image represent the earth's surface in different spectral sampling intervals and have clear physical meanings, while the existing perceptual hash algorithm does not take this into account and cannot perceive the content of each band. Moreover, multispectral images are generally of large sizes (some may be over several GB), while most existing perceptual hash algorithms compute the hash value from an image's global features and are generally not sensitive to local modification in the multispectral remote sensing images. This paper addresses the above problems by presenting a novel perceptual hash algorithm for multispectral remote sensing image authentication. In this paper, we made the following contributions:1. In the proposed hash algorithm, we adopt affinity propagation (AP) clustering to separate the bands into several band subsets to reduce redundancy, in which mutual information (MI) is chosen to measure the similarity of the bands. Therefore, the input image of our algorithm can be of an arbitrary band number to avoid setting the number of band clusters. 2. Based on the analysis of data characteristics of the MS image and the basic principles of perceptual hash, we adopt the band fusion technique to extract the principle features and obtain compact hash values for each band subset, which overcomes the deficiencies in existing perceptual hash algorithms for mono-spectral images. 3. We introduce grid entropy-based adaptive weighted fusion rules to obtain comprehensive features of the grid in the same geographic region. This helps improve the preservation of detailed information as much as possible. The remainder of this paper is organized as follows. Section 2 gives a brief introduction to perceptual hash algorithms and discusses the related work. Section 3 describes our proposed algorithm in detail. Section 4 presents our experimental results and analysis. Finally, we draw conclusions in Section 5.