Deep Learning Based Attack on Phase-Truncated Optical Encoding

Lina Zhou; Xudong Chen; Wen Chen

doi:10.1109/NEMO49486.2020.9343452

Deep Learning Based Attack on Phase-Truncated Optical Encoding

Lina Zhou, Xudong Chen, Wen Chen

The Hong Kong Polytechnic University

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

3 Citations (Scopus)

Abstract

We apply the learning based attack to study the vulnerability of phase-truncated optical encoding scheme. By using a number of ciphertext-plaintext pairs to train a designed learning model, an attacker can effectively analyze the vulnerability of optical encryption scheme based on phase truncation. The learning based attacks for phase-truncated optical encoding can retrieve unknown plaintexts from the given ciphertexts, which can avoid the retrieval of security keys and the design of complex phase retrieval algorithms. It is demonstrated that the learning based attack can provide a promising approach for vulnerability analysis of phase-truncated optical cryptosystems.

Original language	English
Title of host publication	2020 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization, NEMO 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728169668
DOIs	https://doi.org/10.1109/NEMO49486.2020.9343452
Publication status	Published - 7 Dec 2020
Event	2020 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization, NEMO 2020 - Hangzhou, China Duration: 7 Dec 2020 → 9 Dec 2020

Publication series

Name	2020 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization, NEMO 2020

Conference

Conference	2020 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization, NEMO 2020
Country/Territory	China
City	Hangzhou
Period	7/12/20 → 9/12/20

Keywords

learning based attacks
optical encoding
phase truncation

ASJC Scopus subject areas

Computer Networks and Communications
Signal Processing
Electronic, Optical and Magnetic Materials
Control and Optimization
Modelling and Simulation
Instrumentation

More information

10.1109/NEMO49486.2020.9343452

http://hdl.handle.net/10397/107114

Cite this

Zhou, L., Chen, X., & Chen, W. (2020). Deep Learning Based Attack on Phase-Truncated Optical Encoding. In 2020 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization, NEMO 2020 Article 9343452 (2020 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization, NEMO 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NEMO49486.2020.9343452

Zhou, Lina ; Chen, Xudong ; Chen, Wen. / Deep Learning Based Attack on Phase-Truncated Optical Encoding. 2020 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization, NEMO 2020. Institute of Electrical and Electronics Engineers Inc., 2020. (2020 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization, NEMO 2020).

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abstract = "We apply the learning based attack to study the vulnerability of phase-truncated optical encoding scheme. By using a number of ciphertext-plaintext pairs to train a designed learning model, an attacker can effectively analyze the vulnerability of optical encryption scheme based on phase truncation. The learning based attacks for phase-truncated optical encoding can retrieve unknown plaintexts from the given ciphertexts, which can avoid the retrieval of security keys and the design of complex phase retrieval algorithms. It is demonstrated that the learning based attack can provide a promising approach for vulnerability analysis of phase-truncated optical cryptosystems.",

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author = "Lina Zhou and Xudong Chen and Wen Chen",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization, NEMO 2020 ; Conference date: 07-12-2020 Through 09-12-2020",

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Zhou, L, Chen, X & Chen, W 2020, Deep Learning Based Attack on Phase-Truncated Optical Encoding. in 2020 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization, NEMO 2020., 9343452, 2020 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization, NEMO 2020, Institute of Electrical and Electronics Engineers Inc., 2020 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization, NEMO 2020, Hangzhou, China, 7/12/20. https://doi.org/10.1109/NEMO49486.2020.9343452

Deep Learning Based Attack on Phase-Truncated Optical Encoding. / Zhou, Lina; Chen, Xudong; Chen, Wen.
2020 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization, NEMO 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9343452 (2020 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization, NEMO 2020).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AU - Chen, Xudong

AU - Chen, Wen

PY - 2020/12/7

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N2 - We apply the learning based attack to study the vulnerability of phase-truncated optical encoding scheme. By using a number of ciphertext-plaintext pairs to train a designed learning model, an attacker can effectively analyze the vulnerability of optical encryption scheme based on phase truncation. The learning based attacks for phase-truncated optical encoding can retrieve unknown plaintexts from the given ciphertexts, which can avoid the retrieval of security keys and the design of complex phase retrieval algorithms. It is demonstrated that the learning based attack can provide a promising approach for vulnerability analysis of phase-truncated optical cryptosystems.

AB - We apply the learning based attack to study the vulnerability of phase-truncated optical encoding scheme. By using a number of ciphertext-plaintext pairs to train a designed learning model, an attacker can effectively analyze the vulnerability of optical encryption scheme based on phase truncation. The learning based attacks for phase-truncated optical encoding can retrieve unknown plaintexts from the given ciphertexts, which can avoid the retrieval of security keys and the design of complex phase retrieval algorithms. It is demonstrated that the learning based attack can provide a promising approach for vulnerability analysis of phase-truncated optical cryptosystems.

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KW - optical encoding

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T3 - 2020 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization, NEMO 2020

BT - 2020 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization, NEMO 2020

PB - Institute of Electrical and Electronics Engineers Inc.

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Zhou L, Chen X, Chen W. Deep Learning Based Attack on Phase-Truncated Optical Encoding. In 2020 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization, NEMO 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 9343452. (2020 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization, NEMO 2020). doi: 10.1109/NEMO49486.2020.9343452

Deep Learning Based Attack on Phase-Truncated Optical Encoding

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