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1. Hash-chain Compressive Sensing for Secure and Efficient Transmission in Wireless Sensor Networks | |||
Liu Liwei,Peng Haipeng,Li Lixiang,Yang Yixian | |||
Electrics, Communication and Autocontrol Technology 11 January 2019 | |||
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Abstract:With the rapid development of the applications of wireless sensor networks (WSNs) in various fields, such as internet of things, military collaborative operations, e-government, telemedicine, etc., the security, the energy-efficiency and the storage-saving are undoubtedly highlighted in the research of WSNs. Compressive sensing (CS) can compress and reconstruct sparse or compressible signals with fewer samples than those of Nyquist-Shannon theorem requires. In order to meet the requirements of storage, energy-efficiency and security of WSNs simultaneously, we propose an efficient and secure transmission model based on compressive sensing and hash-chain theory, which is called hash-chain compressive sensing (HCCS). Compared with the traditional compressive sensing, only the initial key and the hash function are used in the sensor node to decrease the storage space. And the characteristics of hash-chain assure the security of data transmission under HCCS. Furthermore, we propose an image encryption method based on HCCS in order to improve the efficiency and security of image transmission. The security of image signal is greatly improved by adopting the double-encryption mechanism, which uses the measurement matrix $\Phi _1$ and the encryption matrix $\Phi _2$. The numerical experiments are performed to show the feasibility of HCCS and the effectiveness of the proposed image transmission model. | |||
TO cite this article:Liu Liwei,Peng Haipeng,Li Lixiang, et al. Hash-chain Compressive Sensing for Secure and Efficient Transmission in Wireless Sensor Networks[OL].[11 January 2019] http://en.paper.edu.cn/en_releasepaper/content/4747004 |
2. Detection of resampling in digital images based on correlation coefficients | |||
CAO Hongjian,ZHAO Yao,NI Rongrong | |||
Electrics, Communication and Autocontrol Technology 01 June 2012 | |||
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Abstract:In digital image forensic, resampling detection is a very useful tool because of the common use of resampling in digital image processing. In this paper a new resampling detection algorithm is proposed which explores the periodicity among the rows or columns of the images. The core of the proposed algorithm is the use of correlation coefficients based on the observation that the correlation coefficients of the adjacent rows or columns is periodic in resampled images. In some images, the image contents also show periodicity, which will interfere the detection of resampling. Some measures are taken to reduce that interference. As experiments show, the proposed algorithm is more robust than prior algorithm and shows better results, especially when detecting images which have been through nearest interpolation or resized by ratio 2.0. | |||
TO cite this article:CAO Hongjian,ZHAO Yao,NI Rongrong. Detection of resampling in digital images based on correlation coefficients[OL].[ 1 June 2012] http://en.paper.edu.cn/en_releasepaper/content/4480397 |
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