量子图像水印技术在电力大数据中的应用 |
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作者姓名: | 谭如超 夏候玮明 杨济海 王华 周洋 许建军 |
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作者单位: | 国网江西省电力有限公司信息通信分公司,江西南昌,330077;国网江西省电力有限公司抚州供电分公司,江西抚州,344000 |
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基金项目: | 基于光载射频滤波技术的配电网状态感知系统研究 |
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摘 要: | 为保证电力大数据的安全和处理效果,本文提出一种基于量子图像水印技术的嵌入及提取方法。该方法首先将原载体灰色图像用量子表示,并利用量子小波变换四次分解原载体图像得到子图,再通过量子离散余弦变换对子图进行系数转换。通过对系数矩阵进行奇异值分解得到对角矩阵,再利用量子广义Arnold变换和Logistic映射对水印进行置乱,并进行奇异值分解,从而实现量子水印的嵌入。嵌入的水印图像被分解后,每个像素的灰度信息为一个均衡的量子叠加态,测量后整幅图像为一个均匀的白噪声。水印图像的提取过程为嵌入的逆过程。仿真结果表明,相对经典图像水印,量子图像水印技术计算复杂度更低,计算速度更快,嵌入的水印图像具有很好的安全性,并且不影响载体图像的视觉效果。
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关 键 词: | 电力大数据 量子图像水印 量子变换 奇异值分解 |
收稿时间: | 2018/7/14 0:00:00 |
修稿时间: | 2018/9/22 0:00:00 |
The application of quantum image watermarking technology in power big data |
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Authors: | Tan Ru chao and Xia Hou wei ming |
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Affiliation: | State Grid Jiangxi Electric Power Co., Ltd. Information & Telecommunication Branch,State Grid Jiangxi Electric Power Co., Ltd. Information DdDdamp; Telecommunication Branch |
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Abstract: | In order to ensure the security and processing effect of power big data, this paper presents an embedding and extracting method based on quantum image watermarking technology. In this method, the gray image of the original carrier is first represented by quantum, and the sub-graph is obtained by decomposing the image of the original carrier four times with the quantum wavelet transform. And the coefficients of the sub-graph are transformed by quantum discrete cosine transform. The diagonal matrix is obtained by singular value decomposition of coefficient matrix, then the watermark is scrambled by quantum generalized Arnold transform and Logistic map, and singular value decomposition is carried out to realize the embedding of quantum watermark. After the embedded watermark image is decomposed, the gray level information of each pixel is an equalized quantum superposition state, and the whole image is measured as a uniform white noise. The extraction process of watermark image is an inverse process of embedding. Simulation results show that compared with classical image watermarking, quantum image watermarking has lower computational complexity, faster computation speed, better security of embedded watermark image, and does not affect the visual effect of carrier image. |
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Keywords: | power big data quantum image watermarking quantum transform singular value decomposition |
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