Cryptographic analysis on an optical random-phase-encoding cryptosystem for complex targets based on physics-informed learning

Optical cryptanalysis based on deep learning (DL) has grabbed more and more attention. However, most DL methods are purely data-driven methods, lacking relevant physical priors, resulting in generalization capabilities restrained and limiting practical applications. In this paper, we demonstrate that the double-random phase encoding (DRPE)-based optical cryptosystems are susceptible to preprocessing ciphertext-only attack (pCOA) based on DL strategies, which can achieve high prediction fidelity for complex targets by using only one random phase mask (RPM) for training. After preprocessing the ciphertext information to procure substantial intrinsic information, the physical knowledge DL method based on physical priors is exploited to further learn the statistical invariants in different ciphertexts. As a result, the generalization ability has been significantly improved by increasing the number of training RPMs. This method also breaks the image size limitation of the traditional COA method. Optical experiments demonstrate the feasibility and the effectiveness of the proposed learning-based pCOA method. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Automated summary

This paper discusses optical cryptanalysis based on deep learning and points out the limitations of existing methods. By preprocessing ciphertext information and utilizing physical knowledge deep learning methods, the generalization ability has been improved and the image size limitation of traditional methods has been overcome.