发布时间: 2018-05-16 摘要点击次数: 全文下载次数: DOI: 10.11834/jig.170467 2018 | Volume 23 | Number 5 遥感图像处理

1. 金陵科技学院网络与通信工程学院, 南京 211169;
2. 中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室, 北京 100101;
3. 金陵科技学院数据科学与智慧软件江苏省重点实验室, 南京 211169
 收稿日期: 2017-08-28; 修回日期: 2017-12-07 基金项目: 江苏省自然科学基金项目（BK200170116）；国家自然科学基金项目（41501431，61375121）；金陵科技学院基金项目（jit-fhxm-201604，jit-b-201520）；资源与环境信息系统国家重点实验室开放基金 第一作者简介: 丁凯孟(1985-), 男, 讲师, 2015年于南京师范大学获地图学与地理信息系统理学博士学位, 现为中国科学院地理科学与资源研究所博士后, 主要从事遥感影像完整性认证技术、数字水印等方面的研究。E-mail:dingkaimeng@foxmail.com. 中图法分类号: TP391 文献标识码: A 文章编号: 1006-8961(2018)05-0708-11

# 关键词

Perceptual hash algorithm based on band feature fusion for multispectral remote sensing image authentication
Ding Kaimeng1,2,3, Yang Xiaomei2, Su Shoubao3, Liu Yueming2
1. School of Networks and Tele-Communications Engineering, Jinling Institute of Technology, Nanjing 211169, China;
2. State Key Laboratory of Resource and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
3. Jiangsu Key Laboratory of Data Science and Smart Software, Nanjing 211169, China
Supported by: National Natural Science Foundation of China (41501431, 61375121);Natural Science Foundation of Jiangsu Province, China(BK200170116)

# Abstract

Objective Given the rapid growth of remote sensing techniques, multispectral remote sensing images exhibit increasing potential for more applications. However, a multispectral image can be easily tampered with or forged during transmission and processing because of the widespread use of sophisticated image editing tools, which threatens the integrity of its content and reduces its value. Therefore, ensuring the content credibility and authenticity of multispectral imaged is a major issue before such images are used. However, existing authentication technologies cannot meet requirements because they are sensitive to each bit of the input data. Perceptual hashing, also known as robustness hash, is able to solve the problems of multispectral image content authentication. Perceptual hash has been developed as a frontier research topic in the field of digital media content security and can be applied to image content authentication, image retrieval, image registration, and digital watermarking. Similar to cryptographic hash functions, perceptual hash compresses the representation of the perceptual features of an image to generate a compact feature vector called perceptual hash value, which is a short summary of the perceptual content of an image. Although perceptual hash for the authentication of normal images has been extensively investigated, research on perceptual hashing for multispectral image authentication is limited. The bands of the MS remote sensing image obtain information from the visible and near-infrared spectra of reflected light, which have clear physical meanings. A multispectral image is composed of a set of monochrome images of the same scene, whereas a normal color image is composed of only three monochrome images and grayscale image that has only one channel. The existing perceptual hash algorithm essentially does not take this into account and cannot perceive the content of each band. In light of the data characteristics of the multispectral remote sensing image, a perceptual hash algorithm based on band feature fusion for multispectral image authentication is proposed in this study. Method The algorithm consists of four main stages, i.e., preprocessing, band fusion, feature extraction, and hash value generation. First, taking the large amount of data of multispectral image into consideration, the bands of the multispectral image are partitioned into grids. Given that the tamper location capability is built on the resolution of grid division, the choice of the grid division resolution presents the trade-off between cost and tamper location capability. Second, the grids with the same geographic location are decomposed and fused by two-level discrete wavelet transform, in which different fusion rules are used by low-frequency, intermediate-frequency, and high-frequency components to keep as many fringe features as possible. For intermediate-frequency components, the fusion rule of "maximum first" is selected; for low-frequency and high-frequency components, the adaptive weighted fusion is selected. This stage is intended for encoding the grids of the source bands into a single grid that contains the best aspects of the original grids, which could be suitable for hashing computation. Third, the edge features of the fusion result are extracted based on the Canny operator to construct the edge feature matrix. Given that the hash value has to be as compact and robust as possible to preserve content, the significant singular values are selected as the perceptual features of the fusion result after singular value decomposition on the matrix. Then, the selected singular value is normalized by the hash function to generate the perceptual hash value of the multispectral image. The number of singular values selected depends on the robustness requirement of the algorithm, and the security of the perceptual hash value depends on the selected hash function. The authentication process is implemented through a precise comparison between reconstructed and original perceptual hash values, and the tamper location can be determined if necessary. Result The experiments indicate that the proposed algorithm can achieve content integrity authentication for multispectral remote sensing images with only 32 bytes of authentication information and has good sensitivity to detect local detailed tampering of the multispectral image, such as removing an object, appending an object, and changing an object. The comparison of the hash values of each grid can be used to identify the tamper location and the corresponding geographic region, and the location granularity depends on the resolution of the grid division. By contrast, the proposed algorithm has approximately 100% robustness to lossless compression, has the least significant bit watermark embedding, and has relatively good robustness to lossy compression. In addition, the computational efficiency of the proposed algorithm is doubled that of the existing algorithm. The robustness of the algorithm can be adjusted by setting the number of selected singular values of the feature matrix. Conclusion The experiments and discussions show that the proposed algorithm is sensitive to malicious tampering and is robust to content-preserving operations on multispectral images, whereas the hash value is relatively compact and the computational efficiency is relatively high. The algorithm can meet the requirement of integrity authentication for multispectral remote sensing image.

# Key words

multi-spectral remote sensing images; perceptual hashing; band fusion; integrity authentication; edge features; discrete wavelet transform (DWT)

# 2.2.1 低频信息的融合规则

 ${{\mathit{\alpha }}_{\mathit{k}}}\rm{=}\frac{\mathit{E}_{^{\mathit{wh}}}^{\mathit{k}}}{\sum\limits_{\mathit{i}=1}^{\mathit{N}}{\mathit{E}_{^{\mathit{wh}}}^{\mathit{i}}}}$ (1)

 $\mathit{FL}{{\mathit{L}}_{\mathit{wh}}}\left( \mathit{i}\rm{, }\mathit{j} \right)\rm{=}\sum\limits_{\mathit{k}=1}^{\mathit{N}}{{{\mathit{\alpha }}_{\mathit{k}}}\mathit{FLL}_{^{\mathit{wh}}}^{\mathit{k}}\left( \mathit{i}\rm{, }\mathit{j} \right)}$ (2)

# 2.2.2 中频信息的融合规则

 \begin{align} & \mathit{FH}{{\mathit{L}}_{\mathit{wh}}}\left( \mathit{i}\rm{, }\mathit{j} \right)\rm{=max } \{ \rm{ }\mathit{FHL}_{\mathit{wh}}^{1}\left( \mathit{i}\rm{, }\mathit{j} \right)\rm{, } \\ & \ \ \ \ \mathit{FHL}_{\mathit{wh}}^{2}\left( \mathit{i}\rm{, }\mathit{j} \right)\rm{, }\cdots \rm{, }\mathit{FHL}_{^{\mathit{wh}}}^{\mathit{N}}\left( \mathit{i}\rm{, }\mathit{j} \right)\rm{ } \} \rm{ } \\ \end{align} (3)

# 2.3 特征提取与处理

1) 采用插值算法将融合后的格网单元${\mathit{\boldsymbol{F}}}_{w, h}$的分辨率变为$m$×$m$像素(本文实验中，$m$=64)。此举不仅为了降低计算复杂度，还能降低噪声影响。

2) 过Canny算子提取格网单元$F_{w, h}$的边缘特征，并进行0-1序列化，得到的边缘特征矩阵记为$\mathit{\boldsymbol{ME}}$

3) 为了消除感知哈希鲁棒性与敏感性之间的矛盾，对$\mathit{\boldsymbol{ME}}$进行SVD分解，提取前$K$个奇异值作为格网单元的感知特征。其中，$K$的取值由算法鲁棒性要求和矩阵大小而定，本文实验中选取矩阵前1/4的奇异值。

# 2.4 感知哈希序列的生成

 $\mathit{SH}\rm{=}\ \rm{SHA-256(}{{\mathit{H}}_{\rm{0, 0}}}\left\| {{\mathit{H}}_{\rm{0, 1}}} \right.\left\| \cdots \right\|{{\mathit{H}}_{\mathit{W}\rm{, }\mathit{H}}}\rm{)}$ (4)

 $\mathit{PH}\rm{=}\mathit{SH}\left\| \mathit{P}{{\mathit{H}}_{\rm{0, 0}}} \right.\left\| \mathit{P}{{\mathit{H}}_{\rm{0, 1}}} \right.\left\| \cdots \right\|\mathit{P}{{\mathit{H}}_{\mathit{W}\rm{, }\mathit{H}}}$ (5)

# 2.5 多光谱遥感影像的认证过程

 \left\{ \begin{align} & \rm{影像内容完整}\ \ \ \ \ \ \ \mathit{SH}\rm{=}\mathit{S{H}'} \\ & \rm{影像被篡改}\ \ \ \ \ \ \ \ \ \ \mathit{SH}\ne \mathit{S{H}'} \\ \end{align} \right. (6)

# 3.1 可区分性

Table 1 The comparison of the hash values

 原始哈希序列 影像篡改后的哈希序列 $PH_{w, h}$ $SH$ $PH_{w, h}$ $SH$ 实验影像A 56a2f807 fc64f47dcf7b0f486f0368cf876de5ef002a5537d05764a4ab67e79a5962af93 dfcb813d 10d433c14e06419333b9772fe5e69b954232b6fd86839beaffa4d9f74a38e60d 实验影像B f4216f3e 7eaf6a07f9054c57630767d2216f5e88005d3c812c2c037e9b0b48c0bf1b642a 1dced393 dfe322424e32f23f0108c9a84db22cdb3cb19b19aae6b6b6a40e07e5558aaac7 实验影像C a409141e 4d6a78b5b701d005c1a13d7ab72e6cdc8f2229ac685ebc2fe71b172f7242f3cb 68552808 a7faf5824011bb25513c07c66aa6ed071ee75033e6443f08a58c152fb5a3ab30

# 3.2 鲁棒性

Table 2 The result of robustness test

 /% 影像 无损压缩 水印嵌入 JPEG压缩 (90%压缩) 影像A 6×4划分 100 100 87.5 影像A 12×8划分 100 100 86.4 影像B 100 100 93.7 影像C 100 100 88.3

# 3.3 算法运行效率与安全性分析

Table 3 The computational efficiency

 /s 算法 影像A 6×4划分 影像A 12×8划分 影像B 影像C 文献[20] 16.41 30.32 11.21 34.94 本文 9.76 12.26 6.98 16.41

# 参考文献

• [1] Du B, Zhang L F, Zhang L P, et al. Discriminant manifold learning approach for hyperspectral image dimension reduction[J]. Acta Photonica Sinica, 2013, 42(3): 320–325. [杜博, 张乐飞, 张良培, 等. 高光谱图像降维的判别流形学习方法[J]. 光子学报, 2013, 42(3): 320–325. ] [DOI:10.3788/gzxb20134203.0320]
• [2] Niu X M, Jiao Y H. An overview of perceptual hashing[J]. Acta Electronica Sinica, 2008, 36(7): 1405–1411. [牛夏牧, 焦玉华. 感知哈希综述[J]. 电子学报, 2008, 36(7): 1405–1411. ] [DOI:10.3321/j.issn:0372-2112.2008.07.029]
• [3] Xiang S J, Kim H J, Huang J W. Histogram-based image hashing scheme robust against geometric deformations[C]//Proceedings of the 9th workshop on Multimedia & Security. Dallas, Texas: ACM, 2007: 121-128. [DOI:10.1145/1288869.1288886]
• [4] Sandeep R, Sharma S, Thakur M, et al. Perceptual video hashing based on Tucker decomposition with application to indexing and retrieval of near-identical videos[J]. Multimedia Tools and Applications, 2016, 75(13): 7779–7797. [DOI:10.1007/s11042-015-2695-1]
• [5] Ouyang J, Gao J H, Wen Z K, et al. Video perceptual hashing fuse computational model of human visual system[J]. Journal of Image and Graphics, 2011, 16(10): 1883–1889. [欧阳杰, 高金花, 文振焜, 等. 融合HVS计算模型的视频感知哈希算法研究[J]. 中国图象图形学报, 2011, 16(10): 1883–1889. ] [DOI:10.11834/jig.20111005]
• [6] Zhu Y Y, Wen Z K, Du Y H, et al. Video forgery detection and multi-granularity location based on video perceptual hashing[J]. Journal of Image and Graphics, 2013, 18(8): 924–932. [朱映映, 文振焜, 杜以华, 等. 基于视频感知哈希的视频篡改检测与多粒度定位[J]. 中国图象图形学报, 2013, 18(8): 924–932. ] [DOI:10.11834/jig.20130806]
• [7] Zhang Q Y, Xing P F, Liu Y W, et al. Research on security transmission of perceptual hash values based on ECC and digital watermarking[J]. International Journal of Security and Its Applications, 2015, 9(3): 255–266. [DOI:10.14257/ijsia.2015.9.3.19]
• [8] Saad S M. Design of a robust and secure digital signature scheme for image authentication over wireless channels[J]. IET Information Security, 2009, 3(1): 1–8. [DOI:10.1049/iet-ifs:20070112]
• [9] Lu C S, Liao H Y M. Structural digital signature for image authentication:an incidental distortion resistant scheme[J]. IEEE Transactions on Multimedia, 2003, 5(2): 161–173. [DOI:10.1109/TMM.2003.811621]
• [10] Liu Z Q, Li Q, Liu J R, et al. SIFT based image hashing algorithm[J]. Chinese Journal of Scientific Instrument, 2011, 32(9): 2024–2028. [刘兆庆, 李琼, 刘景瑞, 等. 一种基于SIFT的图像哈希算法[J]. 仪器仪表学报, 2011, 32(9): 2024–2028. ] [DOI:10.19650/j.cnki.cjsi.2011.09.017]
• [11] Xiang S J, Yang J Q. NMF-based image hashing algorithm using restricted random blocking[J]. Journal of Electronics & Information Technology, 2011, 33(2): 337–341. [项世军, 杨建权. 基于约束随机分块的NMF图像哈希算法[J]. 电子与信息学报, 2011, 33(2): 337–341. ] [DOI:10.3724/SP.J.1146.2010.00212]
• [12] Ding K M, Zhu C Q. Perceptual hash algorithm for integrity authentication of remote sensing image[J]. Journal of Southeast University:Natural Science Edition, 2014, 44(4): 723–727. [丁凯孟, 朱长青. 一种用于遥感影像完整性认证的感知哈希算法[J]. 东南大学学报:自然科学版, 2014, 44(4): 723–727. ] [DOI:10.3969/j.issn.1001-0505.2014.04.008]
• [13] Chen L, Li Z, Yang J F. Compressive perceptual hashing tracking[J]. Neurocomputing, 2017, 239: 69–80. [DOI:10.1016/j.neucom.2017.02.004]
• [14] Qin C, Chen X Q, Dong J, et al. Perceptual image hashing with selective sampling for salient structure features[J]. Displays, 2016, 45: 26–37. [DOI:10.1016/j.displa.2016.09.003]
• [15] Yang Y C, Zhou J W, Duan F P, et al. Wave atom transform based image hashing using distributed source coding[J]. Journal of Information Security and Applications, 2016, 31: 75–82. [DOI:10.1016/j.jisa.2016.09.001]
• [16] Tang Z J, Huang L Y, Zhang X Q, et al. Robust image hashing based on color vector angle and canny operator[J]. AEU-International Journal of Electronics and Communications, 2016, 70(6): 833–841. [DOI:10.1016/j.aeue.2016.03.010]
• [17] Yan C P, Pun C M, Yuan X C. Multi-scale image hashing using adaptive local feature extraction for robust tampering detection[J]. Signal Processing, 2016, 121: 1–16. [DOI:10.1016/j.sigpro.2015.10.027]
• [18] Cui C, Mao H K, Niu X M, et al. A novel hashing scheme for Depth-image-based-rendering 3D images[J]. Neurocomputing, 2016, 191: 1–11. [DOI:10.1016/j.neucom.2016.01.028]
• [19] Chen F, Lyu S H, Li J, et al. Multi-label image retrieval by hashing with object proposal[J]. Journal of Image and Graphics, 2017, 22(2): 232–240. [陈飞, 吕绍和, 李军, 等. 目标提取与哈希机制的多标签图像检索[J]. 中国图象图形学报, 2017, 22(2): 232–240. ] [DOI:10.11834/jig.20170211]
• [20] Ding K M, Zhu C Q, Su S B, et al. Perceptual hash algorithm for integrity authentication of multispectral remote sensing images[J]. Optics and Precision Engineering, 2015, 23(10): 676–683. [丁凯孟, 朱长青, 苏守宝, 等. 用于多光谱影像完整性认证的感知哈希算法[J]. 光学精密工程, 2015, 23(10): 676–683. ] [DOI:10.3788/OPE.20152313.0677]
• [21] Liu F Z, Zhang J X, Lin Z J, et al. Measurement of graylevel and texture information in multispectral images[J]. Geomatics and Information Science of Wuhan University, 2016, 41(3): 415–420. [刘凤珠, 张景雄, 林宗坚, 等. 多光谱遥感影像的灰度与纹理信息测度方法[J]. 武汉大学学报:信息科学版, 2016, 41(3): 415–420. ] [DOI:10.13203/j.whugis20140329]
• [22] Zhang L B, Chen J, Qiu B C. Region of interest extraction in remote sensing images by saliency analysis with the normal directional lifting wavelet transform[J]. Neurocomputing, 2016, 179: 186–201. [DOI:10.1016/j.neucom.2015.11.093]
• [23] Song M X, Guo P. A combinatorial optimization method for remote sensing image fusion with Contourlet and HSI transform[J]. Journal of Computer-Aided Design & Computer Graphics, 2012, 24(1): 83–88. [宋梦馨, 郭平. 结合Contourlet和HSI变换的组合优化遥感图像融合方法[J]. 计算机辅助设计与图形学学报, 2012, 24(1): 83–88. ] [DOI:10.3969/j.issn.1003-9775.2012.01.014]
• [24] Yang Y, Tong S, Huang S Y. Image fusion based on fast discrete Curvelet transform[J]. Journal of Image and Graphics, 2015, 20(2): 219–228. [杨勇, 童松, 黄淑英. 快速离散Curvelet变换域的图像融合[J]. 中国图象图形学报, 2015, 20(2): 219–228. ] [DOI:10.11834/jig.20150208]
• [25] Cheng J, Liu H J, Liu T, et al. Remote sensing image fusion via wavelet transform and sparse representation[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 104: 158–173. [DOI:10.1016/j.isprsjprs.2015.02.015]