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

作者机构：Department of Applied PhysicsZhejiang University of Science and TechnologyHangzhou 310023China Department of Optical EngineeringZhejiang A&F UniversityHangzhou 311300China 

出 版 物：《Chinese Physics B》 (中国物理B（英文版）)

年 卷 期：2022年第31卷第9期

页      面：293-300页

核心收录：

学科分类：0710[理学-生物学] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 070207[理学-光学] 07[理学] 08[工学] 081104[工学-模式识别与智能系统] 0835[工学-软件工程] 0803[工学-光学工程] 0811[工学-控制科学与工程] 0702[理学-物理学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

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) 

主　　题：optical encryption nonlinear optical cryptosystem deep learning phase retrieval algorithm 

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 *** 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 *** 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 *** results of numerical experiments with the DenseNet model clearly demonstrate the validity and good performance of the proposed the DL-based attack on NOCs.