Deep-learning-based cryptanalysis of two types of nonlinear optical cryptosystems  

作　　者：Xiao-Gang Wang Hao-Yu Wei 汪小刚;魏浩宇(Department of Applied Physics,Zhejiang University of Science and Technology,Hangzhou 310023,China;Department of Optical Engineering,Zhejiang A&F University,Hangzhou 311300,China)

机构地区：[1]Department of Applied Physics,Zhejiang University of Science and Technology,Hangzhou 310023,China [2]Department of Optical Engineering,Zhejiang A&F University,Hangzhou 311300,China

出　　处：《Chinese Physics B》2022年第9期293-300,共8页中国物理B（英文版）

基　　金：Project supported by the National Natural Science Foundation of China(Grant Nos.61975185 and 61575178);the Natural Science Foundation of Zhejiang Province,China(Grant No.LY19F030004);the Scientific Research and Development Fund of Zhejiang University of Science and Technology,China(Grant No.F701108L03).

摘　　要：The two types of nonlinear optical cryptosystems(NOCs)that are respectively based on amplitude-phase retrieval algorithm(APRA)and phase retrieval algorithm(PRA)have attracted a lot of attention due to their unique mechanism of encryption process and remarkable ability to resist common attacks.In this paper,the securities of the two types of NOCs are evaluated by using a deep-learning(DL)method,where an end-to-end densely connected convolutional network(DenseNet)model for cryptanalysis is developed.The proposed DL-based method is able to retrieve unknown plaintexts from the given ciphertexts by using the trained DenseNet model without prior knowledge of any public or private key.The results of numerical experiments with the DenseNet model clearly demonstrate the validity and good performance of the proposed the DL-based attack on NOCs.

关 键 词：optical encryption nonlinear optical cryptosystem deep learning phase retrieval algorithm 

分 类 号：O439[机械工程—光学工程] TP18[理学—光学]