.png){kind=icon}
.png){kind=icon}
Return
Crypatanalysis of random-phase-encoding-based optical cryptosystem via deep learning
.png){kind=icon}
OA
AI
DOI：10.1364/OE.27.021204.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Random Phase Encoding (RPE) techniques for image encryption have drawn increasing attention during the past decades. We demonstrate in this contribution that the RPE-based optical ciyptosystems are vulnerable to the chosen-plaintext attack (CPA) with deep learning strategy. A deep neural network (DNN) model is employed and trained to learn the working mechanism of optical cryptosystems, and finally obtaining a certain optimized DNN that acts as a decryption system. Numerical simulations were carried out to verify its feasibility and reliability of not only the classical Double RPE (DRPE) scheme but also the security-enhanced Tripe RPE (TRPE) scheme. The results further indicate the possibility of reconstructing images (plaintexts) outside the original data set. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
Keywords:
SECURITY ENHANCEMENT
IMAGE ENCRYPTION
PLAINTEXT ATTACK
LINE