Quasi-Periodic Optical Key-Enabled Hybrid Cryptography: Merging Diffractive Physics and Deep Learning for High-Dimensional Security
Haiqi Gao, Yu Shao, Jiaming Liang, Xuehui Wang, Junren Wen, Yuchuan Shao, Yueguang Zhang, Weidong Shen, Chenying Yang

TL;DR
This paper introduces a novel optical cryptography system combining quasi-periodic physical keys with deep learning for enhanced security, robustness, and efficiency in high-dimensional data encryption.
Contribution
It proposes the Quasi Periodic Optical Key (QPOK) and a deep learning-based reconstruction method, advancing physical key design and optical encryption techniques.
Findings
Reduced inter-class distances by over 50%
Tolerates up to 20% ciphertext loss
Expands cryptographic key space with tunable parameters
Abstract
Optical encryption inherently provides strong security advantages, with hybrid optoelectronic systems offering additional degrees of freedom by integrating optical and algorithmic domains. However, existing optical encryption schemes heavily rely on electronic computation, limiting overall efficiency, while the physical keys are susceptible to damage, compromising both security and system stability. To overcome these challenges, we introduce the Quasi Periodic Optical Key (QPOK), which combines long range order with short range disorder, enabling enhanced security and robustness against damage within a single platform. By leveraging diffraction symmetry, our design enables optics-driven encryption, effectively shifting the optoelectronic balance toward photonic processing. Moreover, we innovatively apply deep learning to reconstruct the complex optical ciphertext field using only…
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Taxonomy
TopicsChaos-based Image/Signal Encryption · Neural Networks and Reservoir Computing · Cryptography and Data Security
