A novel lossless information hiding algorithm based on wavelet neural network for digital vector maps is introduced. Wavelet coefficients being manipulated are embedded into a vector map, which could be restored by ad...A novel lossless information hiding algorithm based on wavelet neural network for digital vector maps is introduced. Wavelet coefficients being manipulated are embedded into a vector map, which could be restored by adjusting the weights of neurons in the designed neural network. When extracting the watermark extraction, those coefficients would be extracted by wavelet decomposition. With the trained multilayer feed forward neural network, the watermark would be obtained finally by measuring the weights of neurons. Experimental results show that the average error coding rate is only 6% for the proposed scheme and compared with other classical algorithms on the same tests, it is indicated that the proposed algorithm hashigher robustness, better invisibility and less loss on precision.展开更多
Distributed secure quantum machine learning （DSQML） enables a classical client with little quantum technology to delegate a remote quantum machine learning to the quantum server with the privacy data preserved. More...Distributed secure quantum machine learning （DSQML） enables a classical client with little quantum technology to delegate a remote quantum machine learning to the quantum server with the privacy data preserved. Moreover, DSQML can be extended to a more general case that the client does not have enough data, and resorts both the remote quantum server and remote databases to perform the secure machi~ learning. Here we propose a DSQML protocol that the client can classify two-dimensional vectors to dif- ferent clusters, resorting to a remote small-scale photon quantum computation processor. The protocol is secure without leaking any relevant information to the Eve. Any eavesdropper who attempts to intercept and disturb the learning process can be noticed. In principle, this protocol can be used to classify high dimensional vectors and may provide a new viewpoint and application for future ＂big data＂.展开更多
文摘A novel lossless information hiding algorithm based on wavelet neural network for digital vector maps is introduced. Wavelet coefficients being manipulated are embedded into a vector map, which could be restored by adjusting the weights of neurons in the designed neural network. When extracting the watermark extraction, those coefficients would be extracted by wavelet decomposition. With the trained multilayer feed forward neural network, the watermark would be obtained finally by measuring the weights of neurons. Experimental results show that the average error coding rate is only 6% for the proposed scheme and compared with other classical algorithms on the same tests, it is indicated that the proposed algorithm hashigher robustness, better invisibility and less loss on precision.
基金supported by the National Natural Science Foundation of China(11474168 and 61401222)the Natural Science Foundation of Jiangsu Province(BK20151502)+1 种基金the Qing Lan Project in Jiangsu Provincea Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘Distributed secure quantum machine learning （DSQML） enables a classical client with little quantum technology to delegate a remote quantum machine learning to the quantum server with the privacy data preserved. Moreover, DSQML can be extended to a more general case that the client does not have enough data, and resorts both the remote quantum server and remote databases to perform the secure machi~ learning. Here we propose a DSQML protocol that the client can classify two-dimensional vectors to dif- ferent clusters, resorting to a remote small-scale photon quantum computation processor. The protocol is secure without leaking any relevant information to the Eve. Any eavesdropper who attempts to intercept and disturb the learning process can be noticed. In principle, this protocol can be used to classify high dimensional vectors and may provide a new viewpoint and application for future ＂big data＂.
