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收稿：2017-08-28，
修回：2017-12-7，
纸质出版：2018-05-16
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目的
2
多光谱遥感影像的完整性、真实性等安全问题逐步受到人们的关注，但是，传统认证技术更多地关注数据载体的认证，其不能满足多光谱遥感影像的认证需求。针对多光谱遥感影像的数据特点，提出一种融合波段感知特征的多光谱遥感影像感知哈希认证算法。
方法
2
首先，采用隐形格网划分将多光谱影像的各个波段划分成不同的区域；然后，采用离散小波变换对各波段相同地理位置的格网单元进行分解，并分别采用不同的融合规则对小波变换后的不同分量进行融合；最后，通过Canny算子与奇异值分解提取融合结果的感知特征，再对提取的感知特征进行归一化，最终生成影像的感知哈希序列。多光谱影像的认证过程通过精确匹配感知哈希序列来实现。
结果
2
本文算法采用Landsat TM影像和高分二号卫星的融合影像数据为实验对象，从摘要性、可区分行、鲁棒性、算法运行效率以及安全性等方面进行测试与分析。结果表明，该算法只需要32字节的认证信息就能够实现多光谱遥感影像的认证，摘要性有了较大提高，且算法运行效率提高约1倍；同时，该算法可以有效检测影像的恶意篡改，并对无损压缩和LSB水印嵌入保持近乎100%的鲁棒性。
结论
2
本文算法克服了现有技术在摘要性、算法运行效率等方面不足，而且有较好的可区分性、鲁棒性，能够用于多光谱遥感影像的完整性认证，尤其适合对摘要性要求较高的环境。
Objective
2
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
2
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
2
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
2
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.
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]
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]
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 http://dx.doi.org/10.1145/1288869.1288886 ]
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]
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]
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]
Zhang Q Y, Xing P F, Liu Y W, et al. Research on security transmission of perceptual hash values basedon 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]
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]
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]
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]
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]
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]
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]
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]
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]
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]
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]
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]
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]
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]
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]
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]
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]
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]
Cheng J, Liu H J, Liu T, et al. Remote sensing image fusion via wavelet transform and sparserepresentation[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 104:158-173.[DOI:10.1016/j.isprsjprs.2015.02.015]
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