丁凯孟,杨晓梅,苏守宝,刘岳明(金陵科技学院网络与通信工程学院, 南京 211169;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;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)
目的 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 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 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 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。
Perceptual hash algorithm based on band feature fusion for multispectral remote sensing image authentication
Ding Kaimeng,Yang Xiaomei,Su Shoubao,Liu Yueming(School of Networks and Tele-Communications Engineering, Jinling Institute of Technology, Nanjing 211169, China;State Key Laboratory of Resource and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;Jiangsu Key Laboratory of Data Science and Smart Software, Nanjing 211169, China)
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.