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Proceedings Paper

Cryptanalysis on double random phase encoding with deep learning
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Paper Abstract

Random-phase-based optical image encryption techniques have drawn a lot of attention in recent years. However, in this contribution those schemes have been demonstrated to be vulnerable to chosen-plaintext attack (CPA) by employing the deep learning strategy. Specifically, by optimizing the parameters, the chosen deep neural network (DNN) can be trained to learn the sensing of an optical cryptosystem and thus get the ability to reconstruct any plaintext image from its corresponding ciphertext. A set of numerical simulation results have been further provided to shown its ability on cracking not only the classical double random phase encryption (DRPE), but also the tripe random-phase encryption (TRPE).

Paper Details

Date Published: 7 September 2018
PDF: 7 pages
Proc. SPIE 10751, Optics and Photonics for Information Processing XII, 107510W (7 September 2018); doi: 10.1117/12.2319902
Show Author Affiliations
Han Hai, Shenzhen Univ. (China)
Meihua Liao, Shenzhen Univ. (China)
Dajiang Lu, Shenzhen Univ. (China)
Wenqi He, Shenzhen Univ. (China)
Xiang Peng, Shenzhen Univ. (China)

Published in SPIE Proceedings Vol. 10751:
Optics and Photonics for Information Processing XII
Abdul A. S. Awwal; Khan M. Iftekharuddin; Mireya García Vázquez, Editor(s)

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