Optical cryptanalysis is essential to the further investigation of more secure optical cryptosystems.Learning-based at-tack of optical encryption eliminates the need for the retrieval of random phase keys of optical e...Optical cryptanalysis is essential to the further investigation of more secure optical cryptosystems.Learning-based at-tack of optical encryption eliminates the need for the retrieval of random phase keys of optical encryption systems but it is limited for practical applications since it requires a large set of plaintext-ciphertext pairs for the cryptosystem to be at-tacked.Here,we propose a two-step deep learning strategy for ciphertext-only attack(COA)on the classical double ran-dom phase encryption(DRPE).Specifically,we construct a virtual DRPE system to gather the training data.Besides,we divide the inverse problem in COA into two more specific inverse problems and employ two deep neural networks(DNNs)to respectively learn the removal of speckle noise in the autocorrelation domain and the de-correlation operation to retrieve the plaintext image.With these two trained DNNs at hand,we show that the plaintext can be predicted in real-time from an unknown ciphertext alone.The proposed learning-based COA method dispenses with not only the retrieval of random phase keys but also the invasive data acquisition of plaintext-ciphertext pairs in the DPRE system.Numerical simulations and optical experiments demonstrate the feasibility and effectiveness of the proposed learning-based COA method.展开更多
Many complex networks exist to facilitate the transport of material or information. In this capacity, the authors are often concerned with the continued flow of material or information when a fraction of the links in ...Many complex networks exist to facilitate the transport of material or information. In this capacity, the authors are often concerned with the continued flow of material or information when a fraction of the links in the complex network is disrupted. In other words, the authors are interested in the robustness of the complex network. In this paper, the authors survey measures of robustness like the average path length, the average clustering coefficient, the global efficiency, the size of largest cluster and use these to analyze the robustness of the bus network in Hanoi, Vietnam. The authors find that the bus network is robust against random failure but sensitive to targeted attack, in agreement with its scale-free character. By examining sharp drops in the average path length within the largest cluster of the Hanoi bus network under successive targeted attack, the authors identify five nodes whose loss lead to the fragmentation of the network into five or six disconnected clusters. These isolated clusters represent geographically the Central, Western, Southern, and Northwestern districts of Hanoi. Special considerations must therefore be given to these five nodes when planners wish to expand the bus network, or make it more robust.展开更多
We introduce a novel model for robustness of complex with a tunable attack information parameter. The random failure and intentional attack known are the two extreme cases of our model. Based on the model, we study th...We introduce a novel model for robustness of complex with a tunable attack information parameter. The random failure and intentional attack known are the two extreme cases of our model. Based on the model, we study the robustness of complex networks under random information and preferential information, respectively. Using the generating function method, we derive the exact value of the critical removal fraction of nodes for the disintegration of networks and the size of the giant component. We show that hiding just a small fraction of nodes randomly can prevent a scale-free network from collapsing and detecting just a small fraction of nodes preferentially can destroy a scale-free network.展开更多
基金financial supports from the National Natural Science Foundation of China(NSFC)(62061136005,61705141,61805152,61875129,61701321)Sino-German Research Collaboration Group(GZ 1391)+2 种基金the Mobility program(M-0044)sponsored by the Sino-German CenterChinese Academy of Sciences(QYZDB-SSW-JSC002)Science and Technology Innovation Commission of Shenzhen(JCYJ20170817095047279)。
文摘Optical cryptanalysis is essential to the further investigation of more secure optical cryptosystems.Learning-based at-tack of optical encryption eliminates the need for the retrieval of random phase keys of optical encryption systems but it is limited for practical applications since it requires a large set of plaintext-ciphertext pairs for the cryptosystem to be at-tacked.Here,we propose a two-step deep learning strategy for ciphertext-only attack(COA)on the classical double ran-dom phase encryption(DRPE).Specifically,we construct a virtual DRPE system to gather the training data.Besides,we divide the inverse problem in COA into two more specific inverse problems and employ two deep neural networks(DNNs)to respectively learn the removal of speckle noise in the autocorrelation domain and the de-correlation operation to retrieve the plaintext image.With these two trained DNNs at hand,we show that the plaintext can be predicted in real-time from an unknown ciphertext alone.The proposed learning-based COA method dispenses with not only the retrieval of random phase keys but also the invasive data acquisition of plaintext-ciphertext pairs in the DPRE system.Numerical simulations and optical experiments demonstrate the feasibility and effectiveness of the proposed learning-based COA method.
文摘Many complex networks exist to facilitate the transport of material or information. In this capacity, the authors are often concerned with the continued flow of material or information when a fraction of the links in the complex network is disrupted. In other words, the authors are interested in the robustness of the complex network. In this paper, the authors survey measures of robustness like the average path length, the average clustering coefficient, the global efficiency, the size of largest cluster and use these to analyze the robustness of the bus network in Hanoi, Vietnam. The authors find that the bus network is robust against random failure but sensitive to targeted attack, in agreement with its scale-free character. By examining sharp drops in the average path length within the largest cluster of the Hanoi bus network under successive targeted attack, the authors identify five nodes whose loss lead to the fragmentation of the network into five or six disconnected clusters. These isolated clusters represent geographically the Central, Western, Southern, and Northwestern districts of Hanoi. Special considerations must therefore be given to these five nodes when planners wish to expand the bus network, or make it more robust.
基金Supported by the National Natural Science Foundation of China under Grant No 70501032.
文摘We introduce a novel model for robustness of complex with a tunable attack information parameter. The random failure and intentional attack known are the two extreme cases of our model. Based on the model, we study the robustness of complex networks under random information and preferential information, respectively. Using the generating function method, we derive the exact value of the critical removal fraction of nodes for the disintegration of networks and the size of the giant component. We show that hiding just a small fraction of nodes randomly can prevent a scale-free network from collapsing and detecting just a small fraction of nodes preferentially can destroy a scale-free network.
