| ELECTROMAGNETISM, OPTICS, ACOUSTICS, HEAT TRANSFER, CLASSICAL MECHANICS, AND FLUID DYNAMICS |
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Deep-learning-based cryptanalysis of two types of nonlinear optical cryptosystems |
| Xiao-Gang Wang(汪小刚)1,2,† and Hao-Yu Wei(魏浩宇)2 |
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 |
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Abstract 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.
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Received: 27 December 2021
Revised: 16 March 2022
Accepted manuscript online: 01 April 2022
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PACS:
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42.30.-d
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(Imaging and optical processing)
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42.30.Kq
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(Fourier optics)
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42.30.Rx
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(Phase retrieval)
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42.30.Va
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(Image forming and processing)
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| Fund: 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), and the Scientific Research and Development Fund of Zhejiang University of Science and Technology, China (Grant No. F701108L03). |
Corresponding Authors:
Xiao-Gang Wang
E-mail: wxg1201@163.com
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Cite this article:
Xiao-Gang Wang(汪小刚) and Hao-Yu Wei(魏浩宇) Deep-learning-based cryptanalysis of two types of nonlinear optical cryptosystems 2022 Chin. Phys. B 31 094202
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