A novel image encryption scheme based on parallel compressive sensing and edge detection embedding technology is proposed to improve visual security. Firstly, the plain image is sparsely represented using the discrete...A novel image encryption scheme based on parallel compressive sensing and edge detection embedding technology is proposed to improve visual security. Firstly, the plain image is sparsely represented using the discrete wavelet transform.Then, the coefficient matrix is scrambled and compressed to obtain a size-reduced image using the Fisher–Yates shuffle and parallel compressive sensing. Subsequently, to increase the security of the proposed algorithm, the compressed image is re-encrypted through permutation and diffusion to obtain a noise-like secret image. Finally, an adaptive embedding method based on edge detection for different carrier images is proposed to generate a visually meaningful cipher image. To improve the plaintext sensitivity of the algorithm, the counter mode is combined with the hash function to generate keys for chaotic systems. Additionally, an effective permutation method is designed to scramble the pixels of the compressed image in the re-encryption stage. The simulation results and analyses demonstrate that the proposed algorithm performs well in terms of visual security and decryption quality.展开更多
Due to the heterogeneity of nodes and edges,heterogeneous network embedding is a very challenging task to embed highly coupled networks into a set of low-dimensional vectors.Existing models either only learn embedding...Due to the heterogeneity of nodes and edges,heterogeneous network embedding is a very challenging task to embed highly coupled networks into a set of low-dimensional vectors.Existing models either only learn embedding vectors for nodes or only for edges.These two methods of embedding learning are rarely performed in the same model,and they both overlook the internal correlation between nodes and edges.To solve these problems,a node and edge joint embedding model is proposed for Heterogeneous Information Networks(HINs),called NEJE.The NEJE model can better capture the latent structural and semantic information from an HIN through two joint learning strategies:type-level joint learning and element-level joint learning.Firstly,node-type-aware structure learning and edge-type-aware semantic learning are sequentially performed on the original network and its line graph to get the initial embedding of nodes and the embedding of edges.Then,to optimize performance,type-level joint learning is performed through the alternating training of node embedding on the original network and edge embedding on the line graph.Finally,a new homogeneous network is constructed from the original heterogeneous network,and the graph attention model is further used on the new network to perform element-level joint learning.Experiments on three tasks and five public datasets show that our NEJE model performance improves by about 2.83%over other models,and even improves by 6.42%on average for the node clustering task on Digital Bibliography&Library Project(DBLP)dataset.展开更多
基金supported by the Key Area R&D Program of Guangdong Province (Grant No.2022B0701180001)the National Natural Science Foundation of China (Grant No.61801127)+1 种基金the Science Technology Planning Project of Guangdong Province,China (Grant Nos.2019B010140002 and 2020B111110002)the Guangdong-Hong Kong-Macao Joint Innovation Field Project (Grant No.2021A0505080006)。
文摘A novel image encryption scheme based on parallel compressive sensing and edge detection embedding technology is proposed to improve visual security. Firstly, the plain image is sparsely represented using the discrete wavelet transform.Then, the coefficient matrix is scrambled and compressed to obtain a size-reduced image using the Fisher–Yates shuffle and parallel compressive sensing. Subsequently, to increase the security of the proposed algorithm, the compressed image is re-encrypted through permutation and diffusion to obtain a noise-like secret image. Finally, an adaptive embedding method based on edge detection for different carrier images is proposed to generate a visually meaningful cipher image. To improve the plaintext sensitivity of the algorithm, the counter mode is combined with the hash function to generate keys for chaotic systems. Additionally, an effective permutation method is designed to scramble the pixels of the compressed image in the re-encryption stage. The simulation results and analyses demonstrate that the proposed algorithm performs well in terms of visual security and decryption quality.
基金supported by the National Natural Science Foundation of China(No.62103143)the Hunan Province Key Research and Development Program(No.2022WK2006)+2 种基金the Special Project for the Construction of Innovative Provinces in Hunan(Nos.2020TP2018 and 2019GK4030)the Young Backbone Teacher of Hunan Province(No.2022101)the Scientific Research Fund of Hunan Provincial Education Department(No.22B0471).
文摘Due to the heterogeneity of nodes and edges,heterogeneous network embedding is a very challenging task to embed highly coupled networks into a set of low-dimensional vectors.Existing models either only learn embedding vectors for nodes or only for edges.These two methods of embedding learning are rarely performed in the same model,and they both overlook the internal correlation between nodes and edges.To solve these problems,a node and edge joint embedding model is proposed for Heterogeneous Information Networks(HINs),called NEJE.The NEJE model can better capture the latent structural and semantic information from an HIN through two joint learning strategies:type-level joint learning and element-level joint learning.Firstly,node-type-aware structure learning and edge-type-aware semantic learning are sequentially performed on the original network and its line graph to get the initial embedding of nodes and the embedding of edges.Then,to optimize performance,type-level joint learning is performed through the alternating training of node embedding on the original network and edge embedding on the line graph.Finally,a new homogeneous network is constructed from the original heterogeneous network,and the graph attention model is further used on the new network to perform element-level joint learning.Experiments on three tasks and five public datasets show that our NEJE model performance improves by about 2.83%over other models,and even improves by 6.42%on average for the node clustering task on Digital Bibliography&Library Project(DBLP)dataset.
